 Can you hear me? Fair enough. Right. So yeah, I'm Matthias. I'm a PhD student at ETH in Zurich. And yeah, I want to present to you today work that I did, which is maybe something a bit unusual. So this is a perspective. This is basically a synthesis of literature. And it is concerned with the collective metabolism that we see in microbial communities and how this might be driven by proteome efficiency of individuals. And before I get into the talk, I wanted to take a quick moment to thank the organizers for letting me speak here. And I also wanted to acknowledge briefly that this work is the product of being in a really friendly and welcoming and open-minded research environment at Airwark and ETH, and particularly wonderful group of researchers and the microbial systems ecology group, who many of us are here. In fact, not all of these people are in the audience. So maybe they are not so great. I don't know why I need to talk to them about this. But usually, these are great people. So come find and talk to them. This group, as some of you might know, is used to be led by Martin Ackermann, who I think you know, or some of you. But since the beginning of this year, it's also co-led by Olga Schubert. And she is awesome, so if you get an opportunity to meet her, take it. So what I wanted to do in this talk was to first unpack these two key concepts, collective metabolism and proteome efficiency. I wanted to talk a little bit about how they are related. And then I wanted to take one particular example of a motif of collective metabolism and show in more detail how this is driven by proteome efficiency of individuals. So talking about collective metabolism, let's do this little hypothetical thought experiment where we are concerned with an ecosystem function of interest, in this case, carbon cycling. And we could think that this is done by an extremely generalist microbe. So this microbe has the capacity to use some sort of redox gradient or sunlight to fix CO2 and other inorganics into biomass. And it also has the capacity to, once this biomass decayed, recycle it back into its constituent substrates. Now in reality, because we can sequence ecosystems, we know that there's actually not a single very generalist organism. But instead, there is a whole community of more specialized organisms. These organisms often interact metabolically. And so this process of carbon cycling becomes a collective process. A collective function of the community. So what's currently really or relatively straightforward for us to do is to determine the composition of this community, so we sort of know who's around. And with a little bit more effort, we can also determine the function of the community, like the rate of carbon cycling. But I think what has become really clear throughout this meeting in the last couple of days is that what we're really interested in is to know who is doing what in these sorts of communities. So we see that the metabolic capacity seem to be distributed among different specialists. So we would like to know who are these specialists? Who is doing what? What are the roles that different community members take? And how do they interact with other community members? So it would be really cool if we could get at something like this, like a structure of collective metabolism. And I think the sort of maybe wishful thinking, grand goal of this community should be that we gather so much knowledge on microbial physiology and ecology that we just look at any sort of ecosystem and then we can predict more or less this map, right? We can basically say, ah, we expect this sort of collective metabolism to go on. And obviously, we're quite a long way away from this, but what we were wondering for this work is, I mean, how far have we come so far? You know, with all of the knowledge that we already have on microbial physiology and ecology, what sort of predictions maybe can we make? So what we did here was we went into the literature on physiology and ecology and we mainly asked three main questions. These questions are in what way are metabolic capacities distributed among these interacting specialists? And why and under which conditions are the metabolic capacities distributed? And the answer that we found that we want to put forward or maybe to suggest is that this specialization that's going on and that underlies this distribution of metabolic capacities, this increases the proteome efficiency of the specialists. And this will be the perfect moment to say what I mean with proteome efficiency. So I want to explain proteome efficiency with one hopefully very straightforward example and I will use these infamous pie charts basically to show how a cell in different conditions would allocate its proteome to various different functions. So let's take this extremely simplified model of metabolism and growth where substrates are being transformed by metabolic proteins into cellular building blocks and energy and then those are being used by ribosomes to make the metabolic proteins, to also make the ribosomes and to make some housekeeping proteins. Let's assume that a large fraction of the metabolic proteins are required to make this one amino acid. And then let's ask what happens if this amino acid becomes available in the environment? Usually cells will take it up, right? And because this only requires a transport and not a whole biosynthetic pathway, we need fewer proteome resources to take up the amino acid. So the point here is that the uptake of an amino acid is more proteome efficient than its biosynthesis and generally any process that fulfills a function but requires less protein than an alternative would be called proteome efficient. And there's one really important consequence of being proteome efficient and that is that now you've freed up resources. These resources are protein biosynthetic resources so ribosomes that can now do something else as well as the substrates that were originally going to synthesize this protein. And now with these freed up resources, a cell can do many things and this really depends on the environment that the cell finds itself in. For example, in the sort of boom and bust environments that Sergei talked about, it might be really helpful to just grow really quickly. So in that case, the freed up resources can be allocated to make more ribosomes and to grow faster. But in a more variable environment, you could reallocate these resources to make proteins, to metabolize a potential future substrate and then lag shorter when the environment switches to this sort of substrate. In more supply rate limited environments or environments that reflect more chemostats, we heard from just today but also from Sergei that here it's really about driving down the concentration of the limiting nutrient. And you can achieve a given uptake rate at a lower concentration if you invest more in transporters. And then finally, you could also not invest these resources or reallocate these resources at all. You would effectively reduce the size of your proteome and reduce the requirements in terms of substrate and energy for maintaining this proteome and this might be particularly important for a cell that's starving. So the point I'm trying to make is really, proteome efficiency is this very versatile thing that can facilitate a whole bunch of fitness relevant trades. So how does it link back to trying to predict the structure of collective metabolism? And the point that we want to make here is that metabolic capacities are distributed among specialists, cells specialize because this makes them more proteome efficient and then in certain ways of specializing, this entails, so it requires or facilitates a metabolic interaction and then it will underlie the sort of collective metabolism. So this is basically the main result of all of this work. This is the, let's say, first tiny step to going towards predicting the structure of collective metabolism from the knowledge that we currently have on microbial physiology or maybe I should say from the knowledge that I had eight months ago when this was finished and sent out to journals. So I think I've already learned some things in the last couple of days where some of the nuances here need to be updated but basically we identified or classified five general motifs of collective metabolism and all of these motifs, cells specialize to become more proteome efficient but then they require or facilitate an interaction, a metabolic interaction. And so because I can't go through all of these in one talk, unless I have like two hours or so, I wanted to focus on one of them but also I wanted to say a word or two about all of them so that hopefully some of them strike your interest. It would be super cool if we could discuss this throughout the rest of this meeting. So generally in this first type of motif, this is going on whenever a part of metabolism happens extracellularly. For example, the degradation of chitin that usually cells or microbes can specialize in a sense that they do not produce this enzyme to break down, for example, a polymer but they can still take up the product. So some people would call this cheating. And then the reason for why they are more proteome efficient is quite obvious because they're simply not producing this enzyme to break down the polymer but that also means that they require another cell to do this for them. So this is one way where specialization then leads to requiring another microbe. This second type or motif of collective metabolism is something that we've heard about quite a lot in this meeting and it is that often catabolic pathways are split between specialist cells. So we've heard about acetate crossfeeding and lactate crossfeeding and so on. Here the idea that is that for example, a sugar catabolizing microbe can specialize in the sense that it can run only a partial pathway. It would then secrete a partially catabolized intermediate like an organic acid. And the point is really that by running a shorter pathway, you can under certain circumstances which I will go into in the last part of the talk, you can generate energy more proteome efficiently but then because you release a not fully catabolized substrate you facilitate an interaction with a different micro community member. In motif three, we are concerned or we talk about the exchange of anabolites like amino acids or vitamins and I think we haven't heard so much about this in this meeting but my wonderful colleague Divya is doing great work on this and she will give a talk on her work next week so you should see that. And these last two motifs are really based on instances where metabolic functions conflict with each other and there's basically two flavors of this in one flavor, the one enzyme or one intermediate or product of one metabolic function really inhibits the enzymes of another metabolic function and in this other flavor, two metabolic functions might require metabolism to run in opposing directions. So for example, autotrophic and heterotrophic metabolism and the pink berries that Otto talked about are basically an instance of this type of motif of collective metabolism. Okay, so I will next go to talk about one motif in more detail, are there any questions? This is a very, I think it's a very nice conceptual sort of framework to think about but also these guys while they're doing this happy world scenario, they're also trying to kill each other with toxins and what more, so that makes the collective protein less efficient I guess. That's true, yeah. I think this is describing basically one side and the happy side of ecology and I think you can start taking these concepts, how much protein do you need to invest to, for example, kill someone? I think you can extend this to also competitive interactions but we haven't gone there yet. What kind of cells are you working on? I, so this is, I think I'm not presenting this well enough so this is the misconception that people usually ask me like what have I done in terms of experiments and really what I've done is I went into the literature and I looked at all sorts of types of model organisms that people work with. A lot of these results are based on your work with E. coli but yeah. Were you here in my second talk? No. Okay, so basically we don't find this for slow growing bacteria. What do you not find? We find that protein want to be inefficient. Yeah. I think we should talk about this more. Not in front of an audience. And I'm really sad I had to miss the first two days. There was another meeting that happened that I really need to go to. But let's talk about it. Yeah. And let's talk about this motif in more detail. So the idea here is again that catabolizing a let's say an electron donor like a sugar cells could specialize to basically oxidize this sugar only partially. There might be an argument that says that this generates energy in a more proteome efficient way than they would release an intermediate and then this intermediate can now cross feed another organism. I wanted to quickly motivate this by saying that we find these sorts of cross feeding in various different systems. We find them for organotrophs in probably aerobic systems in lab communities. We definitely find this in anaerobic systems like the human gut. And we also find this for chemolithotrophs. So not only organotrophs. So here, this is an example of nitrifying organisms and the nitrification process which is basically the oxidation of ammonium to nitrite and nitride to nitrate. And this is usually carried out by two interacting cells. So the question really is why do cells perform this partial catabolism? And people who have thought about this, this is basically the previous generation of systems biologists. And what they want to know is how can we, along this sort of exemplatory pathway, how can we optimally allocate enzymes along this pathway to maximize its rate? And what would be the optimal length of the pathway to maximize energy production? So this pathway basically has an extracellular electron donor at a certain concentration. This gets taken up into the cell by a transporter. Then there's a couple of intracellular oxidation steps that all generate energy. And then the final intermediate product is expelled from the cell again. There's a couple of more parameters that come into play when describing this mathematically. So it's a length of the pathway. This is the number of reaction steps. There's this M, which is the number of intracellular intermediates, a number of ATP generating or energy generating steps, an energy generation rate, and a pathway reaction rate. And the way that this is implemented is extremely simplified. So all of the enzymes have identical characteristics of the same size and the same rate constant. And the reaction rates, I think, for mathematical tractability, I guess, are modeled as linear and irreversible. So there are two main and important ingredients that come into play that were postulated for this model. And this is that there are two pools of limiting resources. Pool one is a limiting amount of intracellular substrate that a cell is allowed to have and allowed to distribute along this pathway. And basically for osmotic reasons, and I think we've seen in Terry's talk that cells take osmotic insults seriously and they try to keep osmotic homeostasis. And the second pool of limited resources is enzymes that can be distributed along this pathway. So in this toy, and not very biologically example of sugar oxidation to CO2, it could look like this for a complete pathway run by a more generalist cell. And now the question is, what happens when we reduce the pathway length? Can we make an argument that now, with the reduced pathway length, we can somehow generate energy more proteome efficiently? And what sort of speaks for that is that by reducing the pathway length, these two pools of limited resources, substrate concentrations, and enzyme concentrations that both affect the rate of the pathway now get sort of allocated among fewer reactions. So the rate of each reaction basically profits in a quadratic manner. But at the same time, we have fewer energy couplings. So what we can see is that if we decrease the length of the pathway, we always get an increase in the pathway reaction rate. And what we also see is that this increase is more dramatic when the electron donor concentration is high. And I think this becomes sort of understandable when we look at how enzymes ought to be optimally distributed along this pathway. So it's really just this dichotomy between the first transporting enzyme and all other enzymes where at low concentrations of the electron donor, most of the enzyme needs to go into the transporter and at high electron donor concentrations, only very few, very little of your limited enzyme pool needs to go to the transporter, and most can go in the rest of the pathway. So basically, if we cut these two steps, OX2 and OX3, we can gain different amounts of enzyme to distribute based on the electron donor concentration. We gain here in dark red very little enzyme to distribute at low electron donor concentrations. And a lot of enzymes to distribute at high electron donor concentrations. So therefore, this increase is more steep when the electron donor concentration is high. And what this also means is that the energy production rate has different optimal pathway lengths for different electron donor concentrations. So at a low concentration, this pathway that we call complete is the optimum for energy production and at high concentrations, a short pathway is optimal. And then we can sort of flip this around to speak about proteome efficiency. We can ask for a given energy production rate, how much protein would you need to invest having a short pathway compared to a long pathway? And this is basically what we see in this plot, where at low electron donor concentrations, the short pathway requires much more protein for a given energy production rate. As we increase the concentration of the electron donor, the short pathway becomes more proteome efficient at generating energy. And I think basically this is something that we see in E. coli, where as we increase glucose concentration, at some point we see overflow metabolism, so a more partial catabolism kick in. And we also see this in the other direction with the example of nitrification, where this usually two-step process of ammonium and nitride oxidation becomes a one-step process at really low, sorry, maybe, becomes carried out by a single organism at really low concentrations of ammonium. And this is then called ComaMox. Now the problem about this I realize is that Martina in her talk showed some data which was that when you increase the concentration of glucose, you actually do not see that the community becomes more structured into a glucose ferment and then organic acid oxidizer. So this is a bit of a problem, so therefore in the coffee break I electrically put together another slide to maybe be able to make sense of this. And this slide has to do with how energy generation steps are distributed along a pathway. So Martina told us that her cultures are always shaking super rapidly. So there should be oxygen, you know? In our toy pathway, the energy generation steps were distributed equally along the pathway. In a real pathway, for example, glucose catabolism, we know that more energy can in principle be gained in oxidative phosphorylation. So any NADH that we get to reduce oxygen, this is where we gain more of the energy. And in glycolysis we don't gain a lot of NADH, but in a TCA cycle we gain more NADH. So basically the quality of the electron acceptor or whether or not there is an electron acceptor tells us how much a catabolic pathway is back loaded in terms of energy production opportunities or front loaded. And I think you can show this quite nicely in this redox tower that gives us a sense of how energetically favorable a redox reaction is. When we oxidize glucose with oxygen, as an acceptor you have this large distance. So really most of the energy can be generated at the end of the pathway. When the electron acceptor is nitrate, this is already less so, and less so with sulfide or sulfur as electron acceptors. And even worse, when there is basically no electron acceptor in the cell can only ferment. So I think what this is basically saying is that the more energy front loaded a pathway becomes the more favorable partial catabolism is. So in anaerobic environments or in the absence of oxygen we should see more of the splitting of catabolic pathways. And so to wrap up, I basically asked in what way a metabolic capacity is distributed in a community and for this particular motif, the answer is really that catabolic pathways can be split between specialists, where one specialist performs a partial catabolism, releases an intermediate, and then this cross feeds another specialist. Question two and three were why and under which conditions are metabolic capacities distributed? And here the answer I think is really that partial catabolic pathways can always run faster and they can generate energy more proteome efficiently if the substrate concentrations are high and when pathways are more energy front loaded. So for example, when poor quality acceptors are around or when there are no external electron acceptors. And so to bring this back into the whole perspective what we set out and hope to do is maybe with the bit of knowledge that we've already gathered what can we do in terms of predicting the structure of collective metabolism. The key here we think is that metabolic capacities are distributed because cells specialize, cells specialize because that makes their metabolism more proteome efficient, but then in various cases these are these five motifs that we describe this entails metabolic interactions. And that's the last thing I wanted to say. So thank you very much for listening in and happy to take questions. Thank you very much for the talk. We have time for a few questions. Yeah, you know, when you split metabolite leaves a cell, okay, a lot of metabolites are lost. Can you repeat when you split? Right, when you split a pathway, a lot of metabolites are lost. How so? What do you mean they're lost? Yeah, yeah. So I guess one could say that this type of interactions really depend also on the environment. How turbulent is an environment? How much spatial structure is there? Can there be a biofilm where diffusion is more limited and metabolites get less lost? And I think this is something, some of the basically conditions to say, oh, how likely is it that we see more collective metabolism versus more generalists and turbulence or something like that is definitely something that... I'm just concerned, but you're using what we know about fast growing organism in very kind of a selective conditions, okay, the cell to grow fast. Applying these to the situation in a community where I'm slow growing, I mean, you're barely surviving and all that, right? And like energy, right? It's a topic often talked about. It's certainly an important issue for slow growing communities, I think for a little bit of everything, but it's absolutely not a concern for like the model organism, like you call it, we're talking about. I mean, generalist way more energy that's needed, right? And in fact, it uses these cytochromes that are inefficient. It's just, it doesn't bother to get energy. No. But mixing these two pictures are dangerous. I think often when I present this as well, then people say, oh, this is about energy. And I feel what I'm trying to do very deliberately is to say it is more about requiring fewer proteins for a process. And I think in general this, even a slow growing cell, if it can decide whether to synthesize or take up an amino acid, I think it would take it up. No, that's not what you see, right? Slow growing organisms, they don't bother to optimize their protein. But they're very different. They don't take up amino acids. They don't take amino acid, they keep that as a biosynthesis. So they're regulated in a bad way. They don't bother to regulate. I couldn't believe it until we saw like for five slow growing organisms, they're all like that. Yeah. I think like in a way it's good that I wasn't around on Tuesday because you wouldn't get the whole talk. Okay, we have time for one more question. I'm less skeptical than Terry. But let me just, the one, the number two which you presented, relied on the simple explanation which you got from the paper, relied on this bilinearity of all the reactions because it's essentially a concentration effect. So when you make your reaction to only one out of two things, you get a factor four effect. How sensitive it is to, what if not all of the internal reactions are, the internal metabolites are so low that they are actually in this regime where they are below K for corresponding reaction. And what happens if you have irreversible reactions? You mentioned that those are two requirements in this simplified model. I think one of the people who did most of this work is Jan Ulrich Kräft and I met him at some point and I asked what happens if you have more reversible, more Michaelis-Menten dynamics. And he says, then it's less mathematically tractable but the results are the same. That's what he told me. And then he showed me some figures that they didn't publish. And yeah. Okay, let's thank Matthias again. Okay, just one announcement before we...