See “Methods” for a detailed model description and parameters.
In the model, we explicitly incorporate a “chemical universe”, metabolism, cell growth and division, genome evolution and a two-dimensional spatial environment that all co-evolve (Fig. 1). We do not predefine fitness (such as a target genotype or biomass reaction to be optimised), but set basic rules for cell growth, reproduction and death. This means whether a mutation is beneficial, neutral or deleterious depends on local environmental conditions and interactions, cellular state and genomic background, all of which are shaped by prior evolution in the model. As a consequence, metabolic or ecological strategies are not predefined, but emerge during evolutionary simulations as microbes evolve and explore the possibilities of the chemical universe and reshape their local environment by metabolite uptake and exchange. This approach allows us to de novo create microbial communities with their own evolutionary histories and study them with access to a perfect digital “fossil record”.
a Genes on a linear genome code for specific metabolic enzymes that catalyse individual reactions of the metabolic network. To express proteins and grow, microbes require two non-substitutable building block metabolites B1 and B2 (red, blue) that do not natively occur in their environment, but can be metabolised from the single provided resource R (green) by expressing the right metabolic pathways. Active transport of metabolites across the cell membrane requires an energy metabolite E (yellow). The genome of a single microbe typically covers a small subset of the complete “chemical universe” of 59 reactions (see Methods). b Microbes compete for space and metabolites on a 45 × 45 lattice. They can reproduce in an adjacent empty space if they meet the minimal division cell size. Here, microbes NE and W of the empty space are too small to reproduce. Upon replication, genomes can mutate through gene duplication and deletion, discovery of new genes, and point mutations that can change the expression rate and kinetic parameters of individual genes. New genes can also be acquired via horizontal gene transfer from nearby microbes. Active transport of metabolites and lysis changes the composition of a microbes’ local environment.
Microbes compete for a single resource molecule R and limited space on a 2d grid, reproducing locally into empty neighbouring sites (Fig. 1a, b). They require two essential (non-substitutable) building block metabolites B1, B2 for cell growth and expressing proteins that perform metabolic functions. Building blocks do not natively occur in the environment, but can be synthesised from the provided resource by expressing relevant metabolic proteins. In addition to building blocks, microbes require energy metabolite E to operate transporter proteins to pump metabolites (such as the provided resource) in and out of the cell. The chemical universe available for evolution to meet these metabolic demands consists of a predefined set of nine metabolites (R, B1, B2, M1−5 and E) connected by 59 reactions (43 conversion reactions and 16 transport reactions, see Methods), that contains many redundant pathways and provides many degrees of freedom to form functional metabolic networks.
Proteins catalyse individual reactions (e.g. 1R → 1M1 + 5E) that can be combined to form metabolic pathways. They are coded on the microbe’s genome, which typically covers only a small subset of all reactions. When cells reproduce the genome can mutate, allowing the metabolic networks to evolve by tuning the rates of individual reactions through point mutations (basal expression rate and kinetic parameters of the enzyme) and gene copy number (gene deletion, duplication). New pathways can be formed by discovering new genes or through horizontal gene transfer from nearby cells.
Cells can reproduce in a neighbouring empty site if they meet a minimal division size (Fig. 1b), with competition biased towards larger cells when multiple cells are eligible. Cell death is modelled as a stochastic process with a basal death rate that is potentially elevated when internal metabolites reach toxic concentrations. Cell lysis releases all internal metabolites into the local environment, which then locally diffuse and become available for other nearby microbes to take up. In this way, microbes change the metabolite composition of their local environment through active transport, passive diffusion across the cell membrane and cell death (see Fig. 1b). Motivated by experimental work11,48 that shows that microscale gradients quickly establish and influence microbial metabolism and community dynamics, we first consider evolution in a spatially structured environment with limited diffusion (mimicking biofilm conditions), and subsequently investigate evolution simulating a well-mixed medium.
We constructed an initial population of “minimally viable” microbes by generating 2025 randomly parameterised genomes coding for metabolic networks that contain a food importer and randomly selected genes to produce both building blocks. We then evolved 60 identical copies of this population in parallel under the exact same conditions for 106 time steps (~4 × 105 generations), while fluxing in food metabolite R at a constant rate at all grid points. Using this model, we examine whether “ecosystem based” metabolic strategies evolve, i.e. cross-feeding species with complementary metabolic networks, or “individual-based” strategies in the form of autonomous microbes that produce all required building blocks.
Diverse metabolic strategies evolve in a simple, constant environment
We investigated the evolution of metabolic strategies with a mechanistic model, first focusing on the effect of contingency with a parallel evolution experiment. The ancestral community consists of microbes with metabolic networks composed of a food importer and randomly selected genes to produce both building blocks, all of which have randomly sampled kinetic parameters and expression rates. During the simulations point mutations fix that tune fluxes through specific reactions, and metabolic networks are extended with reactions that are dedicated to producing energy—which allows increased food uptake—and reactions that process byproducts for more energy and/or building blocks. Furthermore, importers are recruited to recycle building blocks that accumulate in the environment through cell lysis. Thus eventually, all populations evolve efficient, closed metabolic networks that make use of all produced metabolites.
However, mutants with different metabolic repertoires continuously arise and compete for dominance within populations and all populations are highly diverse throughout the evolutionary simulations. As only very few genotypically identical individuals are present at any given time, we found it useful for interpretation and visualisation purposes to classify microbes based on their “metabolic genotype”: a binary representation that indicates the presence or absence for each of the 59 metabolic genes and transporters in the genome. Tracking the abundances of these metabolic genotypes over time shows that the evolutionary dynamics are complex and characterised by clonal interference and frequent hitchhiking, leapfrogging and horizontal gene transfer (see Muller plots in Fig. 2).
a–d Example of population dominated by a single autonomous lineage. a Muller plot showing relative frequencies and phylogenetic relationships of different metabolic genotypes throughout the experiment. Clades of microbes with different metabolic genotypes (colours) continuously evolve, resulting in complex evolutionary dynamics of competition and leapfrogging. b Tracking ancestral relationships with renewing lineage markers shows a continued turnover in markers, indicating that at any point during the simulation all microbes have a recent common ancestor. c Snapshots of spatial environment. d Principal component analysis of single-cell proteomes shows that in these communities all microbes express similar proteins. e–h Example of a population that diversifies in two lineages that cross-feed on essential building blocks. g Lineages form an interleaved pattern in the spatial environment (see Supplementary Fig. 6 and Supplementary Movie 1). h Single-cell proteomes show that these lineages express different metabolic enzymes. i–l Example of a population that switches between autonomous and cross-feeding strategies. Lineage markers are redistributed when a single marker fixes in the whole population. PCAs coloured for lineage markers, and composed per simulation on relative single-cell protein expressions, see “Methods” for details.
To condense these complex dynamics, we use lineage tracking and analyse the cell proteomes of these lineages. This reveals that some of these heterogeneous communities are dominated by a single lineage that performs all the metabolic functions outlined above by itself (see Fig. 2a–d; Supplementary Fig. 1 for lineage markers for all 60 populations), while other communities diversify in two complimentary, cross-feeding lineages that specialise in producing one, and importing the other building block (Fig. 2e–h). These cross-feeding lineages form interleaved patterns in the spatial environment, and quickly mix when separated from each other (see Supplementary Fig. 6 and and Supplementary Movie 1). In some communities, cross-feeding or autonomous epochs that last tens of thousands of generations change by quickly switching strategy (Fig. 2i–l, from here on we refer to “autonomous”, “cross-feeding” and “switching” community types). Switching occurs only occasionally, and typically once either strategy is established in a community it lasts until the end of the simulation. So, even though new mutations continue to fix in the population and metabolic networks remain in flux, the metabolic strategy of a community is very stable.
To investigate the nature of the cross-feeding interaction, we examined whether lineages could survive in absence of the other. Specifically, at different time points after the two lineages emerged, we tested metabolic dependencies in the standing diversity of cross-feeding populations by removing all microbes from one lineage and preventing further mutations to occur in the remaining lineage (Fig. 3a–c). We find that cross-feeding communities generally consist of a major lineage that produces both building blocks and can survive by itself, and a minor lineage that is obligately dependent on the major lineage for one of the building blocks and goes extinct when the major lineage is removed, barring a few rare mutants (Fig. 3c). These dependencies are not constant during the simulation, but can increase, decrease, completely switch direction and change to fully co-dependent, as reflected by large changes in size of the population bottleneck following lineage removal (Fig. 3c) and changes in the ratio that cross-feeding lineages occur in a community during the main experiment (Fig. 2; Supplementary Fig. 1). However, both major and minor lineage nearly always grow faster in the presence of their partner (98,8% of cases that survive, see Fig. 3d, e; Supplementary Fig. 2), supplementing their own metabolism with building blocks produced by the other lineage.
We tested metabolic dependencies in 29 selected populations by removing either lineage at different time points after cross-feeding evolved, and without allowing further mutations to occur. a–d Example of 2 × 10 tests of metabolic dependency in replicate population 23. Times indicated with dashed lines in a. b. When removing the major lineage (pink) at t = 2 × 105, most microbes of the remaining lineage (blue) die out. However, a rare mutant is able to grow by itself, though it cannot import building block 1 and does not reach a high abundance. c Outcome of removing lineages for all time points in (a), with different metabolic genotypes within each lineage indicated with shades of the lineage colour. Typically, the minor lineage goes extinct or contains only few mutants that survive in isolation, reflecting obligate dependency on the major lineage. In contrast, microbes in the major lineage can mostly survive without the minor lineage. These dependencies are not constant over evolutionary time as metabolic genotypes that dominate within each lineage change. Note that directly following lineage removal, all remaining lineages can initially quickly grow on the limited store of building blocks that were produced by partner lineage and are still present in the environment. d Community production rates before and 1500 time steps after lineage removal. All surviving minor and major lineages have higher growth rates in the context of the original cross-feeding population. e Difference in community growth rate for surviving lineages in 484 tests of metabolic dependency in 29 populations. In total, 407 out of 412 (98.8%) tested cases that survive removal have reduced growth rates in isolation. Surviving lineages shown in a–d are highlighted.
At any given time during a simulation mutants with the opposing strategy can be found within a community, but even though simulations last tens of thousands of generations communities only switch strategy a couple of times and most communities (45/60) do not switch at all (Fig. 2; Supplementary Fig. 1). For example, in cross-feeding populations autonomous mutants can easily evolve via horizontal gene transfer between lineages with complementary metabolic networks. When a cross-feeding lineage is removed, such mutants in the remaining lineage can successfully take over and found a new, completely autonomous population, but their growth rates are higher in the context of the original cross-feeding population where they exploit the environment created by the whole ecosystem (Fig. 3d, e; Supplementary Fig. 3). This explains why they cannot replace the resident cross-feeding community in the main evolutionary experiment, where they sometimes reach substantial fractions but are typically only transiently present. Similarly, in autonomous communities gene loss can produce mutants that specialise on producing or importing only one building block, but these fail to invade in the resident population that imports and produces both building blocks. Apparently, both cross-feeding and metabolic autonomy are eco-evolutionary attractors that are stable against invading mutants of the opposed strategy, with only occasional occurrences of populations switching between them. Since all simulations started from the same ancestral community, this shows evolutionary contingency determines what kind of community evolves. Before we further consider the consequences for predicting evolution, we first need to understand exactly what determines which strategy evolves.
The evolution of cross-feeding is not explained by protein cost
The Black Queen Hypothesis explains the evolution of cross-feeding through the adaptive loss of costly biosynthetic genes for metabolites that are produced by community members and publicly available27,28,29. In our simulations, the evolution of cross-feeding is characterised by loss of genes for building block synthesis and/or transporters, and results in smaller genomes for cross-feeding compared with autonomous strategies. As we assume an explicit cost for protein expression and essential building blocks are an “inescapable public good” because they are released into the environment when cells die, evolution of cross-feeding could thus be driven by Black Queen dynamics.
To test this we study the effect of varying the cost for protein expression on the evolution of metabolic strategies. Surprisingly, the emergence of autonomous, cross-feeding and occasionally switching communities is robust to increasing or decreasing the costs of proteins expression an order of magnitude (Supplementary Fig. 4). Although some of the dynamics change (for example, lower expression costs allow larger genomes to evolve and increased expression costs cause the evolutionary dynamics to slow down), both strategies evolve under all conditions and are stable eco-evolutionary attractors. Thus, even though in our model the production of building blocks acts as a public good and protein expression has an explicit cost that can be reduced by gene loss, the evolution of cross-feeding is not driven by gene loss to escape this cost.
Trade-offs emerge during the evolution of metabolic networks
To look for signatures for cross-feeding and autonomous strategies, we further investigated the diversity of metabolic networks that evolved. First, we clustered the final evolved populations at the end of the simulation based on metabolic gene frequencies in each population (see Fig. 4a). This shows that all populations share a core set of five genes for the uptake of the food resource and production and uptake of both building blocks. In cross-feeding communities, the genes for production and uptake of building blocks are only present in subsets of the population, reflecting how these communities have a distributed metabolic network. Note that populations strongly differ in which reaction is recruited to produce energy, and how byproducts from this reaction are further metabolised. Typically, a single dedicated energy reaction fixes in a population. Although clustering is dominated by individual energy generating reactions which clusters autonomous and cross-feeding populations with a few exceptions (Fig. 4a), no single gene acts as a signature for either community type.
a Heatmap showing the frequency of 59 metabolic genes (columns) in 60 evolved communities (rows) at the end of the simulation. Cross-feeding communities (dark blue label) and single-lineage autonomous communities (in mustard) cluster mostly together, but no single gene is associated with either metabolic strategy. Instead, the topology of the evolved metabolic network determines community strategy. b Examples of metabolic networks with different topologies. Topology is determined by the substrate of the energy reaction (resource or building block), and networks with the same topology may differ in the specific reaction used to produce energy and other reactions. All communities that degrade resource R for energy follow the cross-feeding strategy (light blue), while in contrast all autonomous communities degrade building block B1 or B2 for energy (yellow). 15 out of 60 communities switched strategy during the evolutionary simulation (marked with letter “S” in a) in most cases because a mutant with the opposing network topology invaded and replaced the resident population. Some communities are formed by microbes with hybrid metabolic networks that degrade both resource and building block for energy (indicated in green) and can switch strategy without changing network topology.
Next, as metabolic strategies are stable during long-term evolution even though metabolic networks continuously evolve, we consider how these gene frequencies change over the complete duration of the simulation by PCA (Fig. 5; Supplementary Fig. 5). We find that the major component separates cross-feeding and autonomous communities. Moreover, based on the energy reactions recruited by each strategy, the metabolic networks can be classified in two different topologies that associate exclusively with either strategy: networks that degrade the food metabolite for energy (i.e. R → energy + byproduct) are found in cross-feeding communities, and networks that degrade a building block for energy (i.e. B1 or B2 → energy + byproduct) in autonomous communities (Fig. 4b). Further support that links network topology to community strategy comes from communities that switched between strategies. Here, a switch from cross-feeding to autonomous (or vice versa) is accompanied by a simultaneous switch in network topology, and when a switch occurs communities move along the first principal component accordingly (Figs. 5d and 6a). Finally, communities can be composed of microbes with hybrid metabolic networks that degrade building blocks as well as food for energy (Figs. 4, 6). Interestingly, within a hybrid metabolic network one type of energy reaction appears dominant, as communities with such networks follow either a cross-feeding or autonomous strategy and do not mix different strategies within a community. Such communities can also switch strategy (and move accordingly in the PCA) without changing their network topology.
a PCA of gene frequencies of 59 metabolic genes in 58 communities over the whole duration of the experiment. One dot represents one community. For clarity, only the initial community and final time point of the simulations are shown. This separates communities by strategy along the first component, and reveals that topology of the evolved metabolic network determines metabolic strategy of the community: networks with reactions that degrade resource R for energy cross-feed on building blocks, whereas networks with reactions that degrade building block B1 or B2 for energy remain metabolically autonomous and consume all building blocks from the environment. b–d Evolutionary trajectory showing all time points in the PCA for a community that b evolves cross-feeding (community 9), c metabolic autonomy (community 4) and d switches between strategies (community 18). For visualisation purposes outlier populations 59 and 47 were omitted from this analysis (see Supplementary Fig. 5 for analysis including these outliers).
Why do these topologies determine metabolic strategies? The amount of energy available to a microbe is limited, and as a consequence, importing more of one metabolite trades off with importing others, depending on what metabolite is used as an energy precursor. If microbes create energy by degrading the food resource, taking up other metabolites such as building blocks lowers the cell’s energy budget. The amount of additional metabolites that can be imported is thus constrained for metabolic networks with this topology. As building blocks are produced and accumulate in the environment, this creates two niches (i.e. one for each building block) that can be exploited by different lineages. In contrast, when building blocks are degraded for energy, importing them increases the energy budget and does not trade-off with importing the food resource, allowing individual microbes to import both types of building blocks as well as retain their competitive ability for the food resource. Furthermore, as only one of the two building blocks is used for energy in autonomous communities, a cross-feeding scenario with this topology would be inherently asymmetrical and unstable, as one lineage would be dependent on the other for both energy and building blocks, while the other lineage only requires the complementary building block.
The evolution of cross-feeding requires spatial structure
Recent experimental and theoretical work11,48,49,50,51 re-emphasised the importance of spatial structure and local interactions on eco-evolutionary dynamics, and metabolic division of labour in particular. In our evolutionary experiment microbes reshape the composition of the local environment through metabolic activity, and cross-feeding lineages self-organise into interleaved spatial patterns, locally enriching it for one of both building blocks (see Figs. 2, 3b; Supplementary Movie 1 and Supplementary Fig. 6). To test whether spatial structure was necessary for cross-feeding to evolve, we re-ran the experiment 18 times starting from the same ancestral population while simulating well-mixed but otherwise identical conditions. No cross-feeding lineages emerged, even though metabolic networks evolved that reliably associated with cross-feeding strategies in unmixed conditions. Moreover, when we stopped mixing, populations with the cross-feeding-associated topology quickly diversified in two cross-feeding lineages, while communities with an autonomous-associated topology remained metabolically autonomous, signifying that it is the interplay between environmental structure and evolved metabolic constraints that drives cross-feeding.
Finally, reasoning that long-term coexistence might result in increased robustness of the cross-feeding interaction, we tested the ecological and evolutionary stability of cross-feeding communities from the original experiment by transfer to a well-mixed medium. Specifically, we subjected seven randomly chosen cross-feeding populations to well-mixed conditions at varying time steps after cross-feeding evolved, while either allowing or preventing further mutations to occur. In all “ecology-only” tests (i.e. without mutation), cross-feeding is stably maintained, and population size and community productivity increase. The increased productivity makes intuitive sense, as mutants that are less productive are outcompeted and cannot re-appear due to lack of mutations. Moreover, under unmixed conditions, local reproduction and metabolite diffusion limit the interface between both lineages and therefore reduce efficient exchange of building blocks. In contrast, when mutations are allowed under mixed conditions, all cross-feeding communities are quickly taken over by autonomous mutants. Strikingly, the resulting autonomous communities have smaller population sizes and productivity than their ancestral cross-feeding community. This shows that while spatial structure puts an upper limit to the efficiency of cross-feeding, it also protects against autonomous metabolic strategies. Consistent with previous results32,51, we find that spatial structure is needed to evolve and maintain metabolic cross-feeding and also find that whether cross-feeding evolves or not depends on constraints of previous metabolic adaptations. As microbes evolve to produce more energy from either the resource or one of the building blocks, importing one metabolite trades of with importing others. We find that the shape of this trade-off is an evolved property of the metabolic networks and the local environmental niches they construct.
Metabolic strategies are an evolutionary contingency
Our results demonstrate that the topology of the evolved metabolic network, combined with spatial structure, determines whether cross-feeding evolves or not. Which topology evolves in a population is arbitrary and often establishes early on. For simulations where the cost of protein expression is increased, this topology often fixes up to tens of thousands of generations before metabolic networks “mature” by making use of all building blocks that accumulate in the environment. What eco-evolutionary strategy will evolve when microbes finally evolve to tap into that source can be predicted from the evolved topology (see Fig. 6b), realising a fate already cemented earlier in its evolutionary history. However, it is interesting to note that exact prediction is limited by several factors. Firstly, evolution of the topology of the metabolic network is typically “founder controlled”, where the energy reaction that establishes itself first in the community quickly accumulates more beneficial mutations and is never outcompeted by other energy reaction genes that are discovered later on. However, mutants with a different energy type occasionally do invade and replace the original population, changing community fate (Fig. 6a). Secondly, microbes that have a hybrid metabolism can switch between strategies as they evolve and different energy reactions dominate the metabolic network or are lost (Fig. 6a).
a Evolutionary trajectories of example communities (first component from PCA in Fig. 5 v.s. time) towards cross-feeding (negative y-value) or autonomous (positive y-value) strategy, coloured for topology of the metabolic network. Grey lines indicate trajectories of all other communities. Changes in dominant network topology cause a switch in community strategy. b Cartoon of evolutionary trajectories. Earlier metabolic adaptations that fix in the initial population dictate final eco-evolutionary attractor, but are an evolutionary contingency. However, prediction is limited because the duration of each depicted stage is unpredictable, and cases where mutants with an alternate network topology invade and replace the population (dashed arrow in b, population 18 and 21 in a) are possible.
Concluding, a combination of contingency and predictability is manifest in our eco-evolutionary modelling experiment. Given the topology of the metabolic network that evolves, in the long run, and with intermittent metastable states, the type of community which evolves is predictable.
Source: Ecology - nature.com