Uncovering and resolving challenges of quantitative modeling in a simplified community of interacting cells

Designing host-associated microbiomes using the consumer/resource model

A key step toward rational microbiome engineering is in silico sampling of realistic microbial communities that correspond to desired host phenotypes, and vice versa. This remains challenging due to a lack of generative models that simultaneously capture compositions of host-associated microbiomes and host phenotypes. To that end, we present a generative model based on the mechanistic consumer/resource (C/R) framework. In the model, variation in microbial ecosystem composition arises due to differences in the availability of effective resources (inferred latent variables), while species' resource preferences remain conserved. Simultaneously, the latent variables are used to model phenotypic states of hosts. In silico microbiomes generated by our model accurately reproduce universal and dataset-specific statistics of bacterial communities. The model allows us to address three salient questions in host-associated microbial ecologies: (i) which host phenotypes maximally constrain the composition of the host-associated microbiomes? (ii) how context-specific are phenotype/microbiome associations, and (iii) what are plausible microbiome compositions that correspond to desired host phenotypes? Our approach aids the analysis and design of microbial communities associated with host phenotypes of interest.

IMPORTANCE

Generative models are extremely popular in modern biology. They have been used to model the variation of protein sequences, entire genomes, and RNA sequencing profiles. Importantly, generative models have been used to extrapolate and interpolate to unobserved regimes of data to design biological systems with desired properties. For example, there has been a boom in machine-learning models aiding in the design of proteins with user-specified structures or functions. Host-associated microbiomes play important roles in animal health and disease, as well as the productivity and environmental footprint of livestock species. However, there are no generative models of host-associated microbiomes. One chief reason is that off-the-shelf machine-learning models are data hungry, and microbiome studies usually deal with large variability and small sample sizes. Moreover, microbiome compositions are heavily context dependent, with characteristics of the host and the abiotic environment leading to distinct patterns in host-microbiome associations. Consequently, off-the-shelf generative modeling has not been successfully applied to microbiomes.To address these challenges, we develop a generative model for host-associated microbiomes derived from the consumer/resource (C/R) framework. This derivation allows us to fit the model to readily available cross-sectional microbiome profile data. Using data from three animal hosts, we show that this mechanistic generative model has several salient features: the model identifies a latent space that represents variables that determine the growth and, therefore, relative abundances of microbial species. Probabilistic modeling of variation in this latent space allows us to generate realistic in silico microbial communities. The model can assign probabilities to microbiomes, thereby allowing us to discriminate between dissimilar ecosystems. Importantly, the model predictively captures host-associated microbiomes and the corresponding hosts' phenotypes, enabling the design of microbial communities associated with user-specified host characteristics.

Exploring interspecific interaction variability in microbiota: A review

Interspecific interactions are an important component and a strong selective force in microbial communities. Over the past few decades, there has been a growing awareness of the variability in microbial interactions, and various studies are already unraveling the inner working dynamics in microbial communities. This has prompted scientists to develop novel techniques for characterizing the varying interspecific interactions among microbes. Here, we review the precise definitions of pairwise and high-order interactions, summarize the key concepts related to interaction variability, and discuss the strengths and weaknesses of emerging characterization techniques. Specifically, we found that most methods can accurately predict or provide direct information about microbial pairwise interactions. However, some of these methods inevitably mask the underlying high-order interactions in the microbial community. Making reasonable assumptions and choosing a characterization method to explore varying microbial interactions should allow us to better understand and engineer dynamic microbial systems.

Aridity shapes distinct biogeographic and assembly patterns of forest soil bacterial and fungal communities at the regional scale

Climate change is exacerbating drought in arid and semi-arid forest ecosystems worldwide. Soil microorganisms play a key role in supporting forest ecosystem services, yet their response to changes in aridity remains poorly understood. We present results from a study of 84 forests at four south-to-north Loess Plateau sites to assess how increases in aridity level (1- precipitation/evapotranspiration) shapes soil bacterial and fungal diversity and community stability by influencing community assembly. We showed that soil bacterial diversity underwent a significant downward trend at aridity levels >0.39, while fungal diversity decreased significantly at aridity levels >0.62. In addition, the relative abundance of Actinobacteria and Ascomycota increased with higher aridity level, while the relative abundance of Acidobacteria and Basidiomycota showed the opposite trend. Bacterial communities also exhibited higher similarity-distance decay rates across geographic and environmental gradients than did fungal communities. Phylogenetic bin-based community assembly analysis revealed homogeneous selection and dispersal limitation as the two dominant processes in bacterial and fungal assembly. Dispersal limitation of bacterial communities monotonically increased with aridity levels, whereas homogeneous selection of fungal communities monotonically decreased. Importantly, aridity also increased the sensitivity of microbial communities to environmental disturbance and potentially decreased community stability, as evidenced by greater community similarity-environmental distance decay rates, narrower habitat niche breadth, and lower microbial network stability. Our study provides new insights into soil microbial drought response, with implications on the sustainability of ecosystems under environmental stress.

Resource competition predicts assembly of gut bacterial communities in vitro

Microbial community dynamics arise through interspecies interactions, including resource competition, cross-feeding and pH modulation. The individual contributions of these mechanisms to community structure are challenging to untangle. Here we develop a framework to estimate multispecies niche overlaps by combining metabolomics data of individual species, growth measurements in spent media and mathematical models. We applied our framework to an in vitro model system comprising 15 human gut commensals in complex media and showed that a simple model of resource competition accounted for most pairwise interactions. Next, we built a coarse-grained consumer-resource model by grouping metabolomic features depleted by the same set of species and showed that this model predicted the composition of 2-member to 15-member communities with reasonable accuracy. Furthermore, we found that incorporation of cross-feeding and pH-mediated interactions improved model predictions of species coexistence. Our theoretical model and experimental framework can be applied to characterize interspecies interactions in bacterial communities in vitro.

Model-free prediction of microbiome compositions

BACKGROUND

The recent recognition of the importance of the microbiome to the host's health and well-being has yielded efforts to develop therapies that aim to shift the microbiome from a disease-associated state to a healthier one. Direct manipulation techniques of the species' assemblage are currently available, e.g., using probiotics or narrow-spectrum antibiotics to introduce or eliminate specific taxa. However, predicting the species' abundances at the new state remains a challenge, mainly due to the difficulties of deciphering the delicate underlying network of ecological interactions or constructing a predictive model for such complex ecosystems.

RESULTS

Here, we propose a model-free method to predict the species' abundances at the new steady state based on their presence/absence configuration by utilizing a multi-dimensional k-nearest-neighbors (kNN) regression algorithm. By analyzing data from numeric simulations of ecological dynamics, we show that our predictions, which consider the presence/absence of all species holistically, outperform both the null model that uses the statistics of each species independently and a predictive neural network model. We analyze real metagenomic data of human-associated microbial communities and find that by relying on a small number of "neighboring" samples, i.e., samples with similar species assemblage, the kNN predicts the species abundance better than the whole-cohort average. By studying both real metagenomic and simulated data, we show that the predictability of our method is tightly related to the dissimilarity-overlap relationship of the training data.

CONCLUSIONS

Our results demonstrate how model-free methods can prove useful in predicting microbial communities and may facilitate the development of microbial-based therapies. Video Abstract.

Bacterial adenine cross-feeding stems from a purine salvage bottleneck

Diverse ecosystems host microbial relationships that are stabilized by nutrient cross-feeding. Cross-feeding can involve metabolites that should hold value for the producer. Externalization of such communally valuable metabolites is often unexpected and difficult to predict. Previously, we discovered purine externalization by Rhodopseudomonas palustris by its ability to rescue an Escherichia coli purine auxotroph. Here we found that an E. coli purine auxotroph can stably coexist with R. palustris due to purine cross-feeding. We identified the cross-fed purine as adenine. Adenine was externalized by R. palustris under diverse growth conditions. Computational modeling suggested that adenine externalization occurs via diffusion across the cytoplasmic membrane. RNAseq analysis led us to hypothesize that adenine accumulation and externalization stem from a salvage pathway bottleneck at the enzyme encoded by apt. Ectopic expression of apt eliminated adenine externalization, supporting our hypothesis. A comparison of 49 R. palustris strains suggested that purine externalization is relatively common, with 16 strains exhibiting the trait. Purine externalization was correlated with the genomic orientation of apt, but apt orientation alone could not always explain purine externalization. Our results provide a mechanistic understanding of how a communally valuable metabolite can participate in cross-feeding. Our findings also highlight the challenge in identifying genetic signatures for metabolite externalization.

Collective production of hydrogen sulfide gas enables budding yeast lacking MET17 to overcome their metabolic defect

Assimilation of sulfur is vital to all organisms. In S. cerevisiae, inorganic sulfate is first reduced to sulfide, which is then affixed to an organic carbon backbone by the Met17 enzyme. The resulting homocysteine can then be converted to all other essential organosulfurs such as methionine, cysteine, and glutathione. This pathway has been known for nearly half a century, and met17 mutants have long been classified as organosulfur auxotrophs, which are unable to grow on sulfate as their sole sulfur source. Surprisingly, we found that met17Δ could grow on sulfate, albeit only at sufficiently high cell densities. We show that the accumulation of hydrogen sulfide gas underpins this density-dependent growth of met17Δ on sulfate and that the locus YLL058W (HSU1) enables met17Δ cells to assimilate hydrogen sulfide. Hsu1 protein is induced during sulfur starvation and under exposure to high sulfide concentrations in wild-type cells, and the gene has a pleiotropic role in sulfur assimilation. In a mathematical model, the low efficiency of sulfide assimilation in met17Δ can explain the observed density-dependent growth of met17Δ on sulfate. Thus, having uncovered and explained the paradoxical growth of a commonly used "auxotroph," our findings may impact the design of future studies in yeast genetics, metabolism, and volatile-mediated microbial interactions.

Microbial interactions in theory and practice: when are measurements compatible with models?

Most predictive models of ecosystem dynamics are based on interactions between organisms: their influence on each other's growth and death. We review here how theoretical approaches are used to extract interaction measurements from experimental data in microbiology, particularly focusing on the generalised Lotka-Volterra (gLV) framework. Though widely used, we argue that the gLV model should be avoided for estimating interactions in batch culture - the most common, simplest and cheapest in vitro approach to culturing microbes. Fortunately, alternative approaches offer a way out of this conundrum. Firstly, on the experimental side, alternatives such as the serial-transfer and chemostat systems more closely match the theoretical assumptions of the gLV model. Secondly, on the theoretical side, explicit organism-environment interaction models can be used to study the dynamics of batch-culture systems. We hope that our recommendations will increase the tractability of microbial model systems for experimentalists and theoreticians alike.

Decreased soil multifunctionality is associated with altered microbial network properties under precipitation reduction in a semiarid grassland

Our results reveal different responses of soil multifunctionality to increased and decreased precipitation. By linking microbial network properties to soil functions, we also show that network complexity and potentially competitive interactions are key drivers of soil multifunctionality.

Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics

Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current models based on ecological theory fail to capture complex community behaviors due to higher order interactions, do not scale well with increasing complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of recurrent neural networks, the LSTM learns a high dimensional data-driven non-linear dynamical system model. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model based on ecological theory. We build methods to decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to design communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions.

ChemChaste: Simulating spatially inhomogeneous biochemical reaction-diffusion systems for modeling cell-environment feedbacks

BACKGROUND

Spatial organization plays an important role in the function of many biological systems, from cell fate specification in animal development to multistep metabolic conversions in microbial communities. The study of such systems benefits from the use of spatially explicit computational models that combine a discrete description of cells with a continuum description of one or more chemicals diffusing within a surrounding bulk medium. These models allow the in silico testing and refinement of mechanistic hypotheses. However, most existing models of this type do not account for concurrent bulk and intracellular biochemical reactions and their possible coupling.

CONCLUSIONS

Here, we describe ChemChaste, an extension for the open-source C++ computational biology library Chaste. ChemChaste enables the spatial simulation of both multicellular and bulk biochemistry by expanding on Chaste's existing capabilities. In particular, ChemChaste enables (i) simulation of an arbitrary number of spatially diffusing chemicals, (ii) spatially heterogeneous chemical diffusion coefficients, and (iii) inclusion of both bulk and intracellular biochemical reactions and their coupling. ChemChaste also introduces a file-based interface that allows users to define the parameters relating to these functional features without the need to interact directly with Chaste's core C++ code. We describe ChemChaste and demonstrate its functionality using a selection of chemical and biochemical exemplars, with a focus on demonstrating increased ability in modeling bulk chemical reactions and their coupling with intracellular reactions.

AVAILABILITY AND IMPLEMENTATION

ChemChaste version 1.0 is a free, open-source C++ library, available via GitHub at https://github.com/OSS-Lab/ChemChaste under the BSD license, on the Zenodo archive at zendodo doi, as well as on BioTools (biotools:chemchaste) and SciCrunch (RRID:SCR022208) databases.

Competition for fluctuating resources reproduces statistics of species abundance over time across wide-ranging microbiotas

Across diverse microbiotas, species abundances vary in time with distinctive statistical behaviors that appear to generalize across hosts, but the origins and implications of these patterns remain unclear. Here, we show that many of these macroecological patterns can be quantitatively recapitulated by a simple class of consumer-resource models, in which the metabolic capabilities of different species are randomly drawn from a common statistical distribution. Our model parametrizes the consumer-resource properties of a community using only a small number of global parameters, including the total number of resources, typical resource fluctuations over time, and the average overlap in resource-consumption profiles across species. We show that variation in these macroscopic parameters strongly affects the time series statistics generated by the model, and we identify specific sets of global parameters that can recapitulate macroecological patterns across wide-ranging microbiotas, including the human gut, saliva, and vagina, as well as mouse gut and rice, without needing to specify microscopic details of resource consumption. These findings suggest that resource competition may be a dominant driver of community dynamics. Our work unifies numerous time series patterns under a simple model, and provides an accessible framework to infer macroscopic parameters of effective resource competition from longitudinal studies of microbial communities.

Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure

Uncovering the general principles that govern the structure of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries can help illuminate this problem by enabling the exploration of chemical reaction universes that are constrained by general mathematical rules. Here, we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We developed a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET), to study string chemistries using tools from the field of stoichiometric constraint-based modeling. In addition to exploring the topological characteristics of different string chemistry networks, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental nutrients. We found that the composition of these minimal metabolic networks was influenced more strongly by the metabolites in the biomass reaction than the identities of the environmental nutrients. This finding has important implications for the reconstruction of organismal metabolic networks and could help us better understand the rise and evolution of biochemical organization. More generally, our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of microbial communities.

Towards a deeper understanding of microbial communities: integrating experimental data with dynamic models

Microbial communities and their functions are shaped by complex networks of interactions among microbes and with their environment. While the critical roles microbial communities play in numerous environments have become increasingly appreciated, we have a very limited understanding of their interactions and how these interactions combine to generate community-level behaviors. This knowledge gap hinders our ability to predict community responses to perturbations and to design interventions that manipulate these communities to our benefit. Dynamic models are promising tools to address these questions. We review existing modeling techniques to construct dynamic models of microbial communities at different scales and suggest ways to leverage multiple types of models and data to facilitate our understanding and engineering of microbial communities.

Multi-stable bacterial communities exhibit extreme sensitivity to initial conditions

Microbial communities can have dramatically different compositions even among similar environments. This might be due to the existence of multiple alternative stable states, yet there exists little experimental evidence supporting this possibility. Here, we gathered a large collection of absolute population abundances capturing population dynamics in one- to four-strain communities of soil bacteria with a complex life cycle in a feast-or-famine environment. This dataset led to several observations: (i) some pairwise competitions resulted in bistability with a separatrix near a 1:1 initial ratio across a range of population densities; (ii) bistability propagated to multi-stability in multispecies communities; and (iii) replicate microbial communities reached different stable states when starting close to initial conditions separating basins of attraction, indicating finite-sized regions where the dynamics are unpredictable. The generalized Lotka-Volterra equations qualitatively captured most competition outcomes but were unable to quantitatively recapitulate the observed dynamics. This was partly due to complex and diverse growth dynamics in monocultures that ranged from Allee effects to nonmonotonic behaviors. Overall, our results highlight that multi-stability might be generic in multispecies communities and, combined with ecological noise, can lead to unpredictable community assembly, even in simple environments.

Combining SIMS and mechanistic modelling to reveal nutrient kinetics in an algal-bacterial mutualism

Microbial communities are of considerable significance for biogeochemical processes, for the health of both animals and plants, and for biotechnological purposes. A key feature of microbial interactions is the exchange of nutrients between cells. Isotope labelling followed by analysis with secondary ion mass spectrometry (SIMS) can identify nutrient fluxes and heterogeneity of substrate utilisation on a single cell level. Here we present a novel approach that combines SIMS experiments with mechanistic modelling to reveal otherwise inaccessible nutrient kinetics. The method is applied to study the onset of a synthetic mutualistic partnership between a vitamin B₁₂-dependent mutant of the alga Chlamydomonas reinhardtii and the B₁₂-producing, heterotrophic bacterium Mesorhizobium japonicum, which is supported by algal photosynthesis. Results suggest that an initial pool of fixed carbon delays the onset of mutualistic cross-feeding; significantly, our approach allows the first quantification of this expected delay. Our method is widely applicable to other microbial systems, and will contribute to furthering a mechanistic understanding of microbial interactions.

Community composition of microbial microcosms follows simple assembly rules at evolutionary timescales

Managing and engineering microbial communities relies on the ability to predict their composition. While progress has been made on predicting compositions on short, ecological timescales, there is still little work aimed at predicting compositions on evolutionary timescales. Therefore, it is still unknown for how long communities typically remain stable after reaching ecological equilibrium, and how repeatable and predictable are changes when they occur. Here, we address this knowledge gap by tracking the composition of 87 two- and three-species bacterial communities, with 3-18 replicates each, for ~400 generations. We find that community composition typically changed during evolution, but that the composition of replicate communities remained similar. Furthermore, these changes were predictable in a bottom-up approach-changes in the composition of trios were consistent with those that occurred in pairs during coevolution. Our results demonstrate that simple assembly rules can hold even on evolutionary timescales, suggesting it may be possible to forecast the evolution of microbial communities.

Pleiotropic mutations can rapidly evolve to directly benefit self and cooperative partner despite unfavorable conditions

Cooperation, paying a cost to benefit others, is widespread. Cooperation can be promoted by pleiotropic ‘win-win’ mutations which directly benefit self (self-serving) and partner (partner-serving). Previously, we showed that partner-serving should be defined as increased benefit supply rate per intake benefit. Here, we report that win-win mutations can rapidly evolve even under conditions unfavorable for cooperation. Specifically, in a well-mixed environment we evolved engineered yeast cooperative communities where two strains exchanged costly metabolites, lysine and hypoxanthine. Among cells that consumed lysine and released hypoxanthine, ecm21 mutations repeatedly arose. ecm21 is self-serving, improving self’s growth rate in limiting lysine. ecm21 is also partner-serving, increasing hypoxanthine release rate per lysine consumption and the steady state growth rate of partner and of community. ecm21 also arose in monocultures evolving in lysine-limited chemostats. Thus, even without any history of cooperation or pressure to maintain cooperation, pleiotropic win-win mutations may readily evolve to promote cooperation.

Metabolic excretion associated with nutrient-growth dysregulation promotes the rapid evolution of an overt metabolic defect

In eukaryotes, conserved mechanisms ensure that cell growth is coordinated with nutrient availability. Overactive growth during nutrient limitation ("nutrient-growth dysregulation") can lead to rapid cell death. Here, we demonstrate that cells can adapt to nutrient-growth dysregulation by evolving major metabolic defects. Specifically, when yeast lysine-auxotrophic mutant lys- encountered lysine limitation, an evolutionarily novel stress, cells suffered nutrient-growth dysregulation. A subpopulation repeatedly evolved to lose the ability to synthesize organosulfurs (lys-orgS-). Organosulfurs, mainly reduced glutathione (GSH) and GSH conjugates, were released by lys- cells during lysine limitation when growth was dysregulated, but not during glucose limitation when growth was regulated. Limiting organosulfurs conferred a frequency-dependent fitness advantage to lys-orgS- by eliciting a proper slow growth program, including autophagy. Thus, nutrient-growth dysregulation is associated with rapid organosulfur release, which enables the selection of organosulfur auxotrophy to better tune cell growth to the metabolic environment. We speculate that evolutionarily novel stresses can trigger atypical release of certain metabolites, setting the stage for the evolution of new ecological interactions.

Synthetic Symbiosis under Environmental Disturbances

The power of synthetic biology is immense. Will it, however, be able to withstand the environmental pressures once released in the wild. As new technologies aim to do precisely the same, we use a much simpler model to test mathematically the effect of a changing environment on a synthetic biological system. We assume that the system is successful if it maintains proportions close to what we observe in the laboratory. Extreme deviations from the expected equilibrium are possible as the environment changes. Our study provides the conditions and the designer specifications which may need to be incorporated in the synthetic systems if we want such “ecoblocs” to survive in the wild.

By virtue of complex ecologies, the behavior of mutualisms is challenging to study and nearly impossible to predict. However, laboratory engineered mutualistic systems facilitate a better understanding of their bare essentials. On the basis of an abstract theoretical model and a modifiable experimental yeast system, we explore the environmental limits of self-organized cooperation based on the production and use of specific metabolites. We develop and test the assumptions and stability of the theoretical model by leveraging the simplicity of an artificial yeast system as a simple model of mutualism. We examine how one-off, recurring, and permanent changes to an ecological niche affect a cooperative interaction and change the population composition of an engineered mutualistic system. Moreover, we explore how the cellular burden of cooperating influences the stability of mutualism and how environmental changes shape this stability. Our results highlight the fragility of mutualisms and suggest interventions, including those that rely on the use of synthetic biology.

IMPORTANCE The power of synthetic biology is immense. Will it, however, be able to withstand the environmental pressures once released in the wild. As new technologies aim to do precisely the same, we use a much simpler model to test mathematically the effect of a changing environment on a synthetic biological system. We assume that the system is successful if it maintains proportions close to what we observe in the laboratory. Extreme deviations from the expected equilibrium are possible as the environment changes. Our study provides the conditions and the designer specifications which may need to be incorporated in the synthetic systems if we want such “ecoblocs” to survive in the wild.

A multidimensional perspective on microbial interactions

Beyond being simply positive or negative, beneficial or inhibitory, microbial interactions can involve a diverse set of mechanisms, dependencies and dynamical properties. These more nuanced features have been described in great detail for some specific types of interactions, (e.g. pairwise metabolic cross-feeding, quorum sensing or antibiotic killing), often with the use of quantitative measurements and insight derived from modeling. With a growing understanding of the composition and dynamics of complex microbial communities for human health and other applications, we face the challenge of integrating information about these different interactions into comprehensive quantitative frameworks. Here, we review the literature on a wide set of microbial interactions, and explore the potential value of a formal categorization based on multidimensional vectors of attributes. We propose that such an encoding can facilitate systematic, direct comparisons of interaction mechanisms and dependencies, and we discuss the relevance of an atlas of interactions for future modeling and rational design efforts.

Delayed antibiotic exposure induces population collapse in enterococcal communities with drug-resistant subpopulations

The molecular underpinnings of antibiotic resistance are increasingly understood, but less is known about how these molecular events influence microbial dynamics on the population scale. Here, we show that the dynamics of E. faecalis communities exposed to antibiotics can be surprisingly rich, revealing scenarios where increasing population size or delaying drug exposure can promote population collapse. Specifically, we demonstrate how density-dependent feedback loops couple population growth and antibiotic efficacy when communities include drug-resistant subpopulations, leading to a wide range of behavior, including population survival, collapse, or one of two qualitatively distinct bistable behaviors where survival is favored in either small or large populations. These dynamics reflect competing density-dependent effects of different subpopulations, with growth of drug-sensitive cells increasing but growth of drug-resistant cells decreasing effective drug inhibition. Finally, we demonstrate how populations receiving immediate drug influx may sometimes thrive, while identical populations exposed to delayed drug influx collapse.

A minimal model for microbial biodiversity can reproduce experimentally observed ecological patterns

Surveys of microbial biodiversity such as the Earth Microbiome Project (EMP) and the Human Microbiome Project (HMP) have revealed robust ecological patterns across different environments. A major goal in ecology is to leverage these patterns to identify the ecological processes shaping microbial ecosystems. One promising approach is to use minimal models that can relate mechanistic assumptions at the microbe scale to community-level patterns. Here, we demonstrate the utility of this approach by showing that the Microbial Consumer Resource Model (MiCRM) - a minimal model for microbial communities with resource competition, metabolic crossfeeding and stochastic colonization - can qualitatively reproduce patterns found in survey data including compositional gradients, dissimilarity/overlap correlations, richness/harshness correlations, and nestedness of community composition. By using the MiCRM to generate synthetic data with different environmental and taxonomical structure, we show that large scale patterns in the EMP can be reproduced by considering the energetic cost of surviving in harsh environments and HMP patterns may reflect the importance of environmental filtering in shaping competition. We also show that recently discovered dissimilarity-overlap correlations in the HMP likely arise from communities that share similar environments rather than reflecting universal dynamics. We identify ecologically meaningful changes in parameters that alter or destroy each one of these patterns, suggesting new mechanistic hypotheses for further investigation. These findings highlight the promise of minimal models for microbial ecology.

Interaction variability shapes succession of synthetic microbial ecosystems

Cellular interactions are a major driver for the assembly and functioning of microbial communities. Their strengths are shown to be highly variable in nature; however, it is unclear how such variations regulate community behaviors. Here we construct synthetic Lactococcus lactis consortia and mathematical models to elucidate the role of interaction variability in ecosystem succession and to further determine if casting variability into modeling empowers bottom-up predictions. For a consortium of bacteriocin-mediated cooperation and competition, we find increasing the variations of cooperation, from either altered labor partition or random sampling, drives the community into distinct structures. When the cooperation and competition are additionally modulated by pH, ecosystem succession becomes jointly controlled by the variations of both interactions and yields more diversified dynamics. Mathematical models incorporating variability successfully capture all of these experimental observations. Our study demonstrates interaction variability as a key regulator of community dynamics, providing insights into bottom-up predictions of microbial ecosystems.

Cellular interactions are a major driver of microbial communities and shown highly variable in strength. Here the authors construct synthetic consortia and mathematical models to elucidate the role of interaction variability in driving ecosystem succession.

Synthetic, Context-Dependent Microbial Consortium of Predator and Prey

Synthetic microbial consortia are a rapidly growing area of synthetic biology. So far, most consortia are designed without considering their environments; however, in nature, microbial interactions are constantly modulated by cellular contexts, which, in principle, can dramatically alter community behaviors. Here we present the construction, validation, and characterization of an engineered bacterial predator-prey consortium that involves a chloramphenicol (CM)-mediated, context-dependent cellular interaction. We show that varying the CM level in the environment can induce success in the ecosystem with distinct patterns from predator dominance to prey-predator crossover to ecosystem collapse. A mathematical model successfully captures the essential dynamics of the experimentally observed patterns. We also illustrate that such a dependence enriches community dynamics under different initial conditions and further test the resistance of the consortium to invasion with engineered bacterial strains. This work exemplifies the role of the context dependence of microbial interactions in modulating ecosystem dynamics, underscoring the importance of including contexts into the design of engineered ecosystems for synthetic biology applications.

Use and abuse of correlation analyses in microbial ecology

Correlation analyses are often included in bioinformatic pipelines as methods for inferring taxon-taxon interactions. In this perspective, we highlight the pitfalls of inferring interactions from covariance and suggest methods, study design considerations, and additional data types for improving high-throughput interaction inferences. We conclude that correlation, even when augmented by other data types, almost never provides reliable information on direct biotic interactions in real-world ecosystems. These bioinformatically inferred associations are useful for reducing the number of potential hypotheses that we might test, but will never preclude the necessity for experimental validation.

Disentangling strictly self-serving mutations from win-win mutations in a mutualistic microbial community

Mutualisms can be promoted by pleiotropic win-win mutations which directly benefit self (self-serving) and partner (partner-serving). Intuitively, partner-serving phenotype could be quantified as an individual's benefit supply rate to partners. Here, we demonstrate the inadequacy of this thinking, and propose an alternative. Specifically, we evolved well-mixed mutualistic communities where two engineered yeast strains exchanged essential metabolites lysine and hypoxanthine. Among cells that consumed lysine and released hypoxanthine, a chromosome duplication mutation seemed win-win: it improved cell's affinity for lysine (self-serving), and increased hypoxanthine release rate per cell (partner-serving). However, increased release rate was due to increased cell size accompanied by increased lysine utilization per birth. Consequently, total hypoxanthine release rate per lysine utilization (defined as 'exchange ratio') remained unchanged. Indeed, this mutation did not increase the steady state growth rate of partner, and is thus solely self-serving during long-term growth. By extension, reduced benefit production rate by an individual may not imply cheating.

Simulations reveal challenges to artificial community selection and possible strategies for success

Multispecies microbial communities often display "community functions" arising from interactions of member species. Interactions are often difficult to decipher, making it challenging to design communities with desired functions. Alternatively, similar to artificial selection for individuals in agriculture and industry, one could repeatedly choose communities with the highest community functions to reproduce by randomly partitioning each into multiple "Newborn" communities for the next cycle. However, previous efforts in selecting complex communities have generated mixed outcomes that are difficult to interpret. To understand how to effectively enact community selection, we simulated community selection to improve a community function that requires 2 species and imposes a fitness cost on one or both species. Our simulations predict that improvement could be easily stalled unless various aspects of selection are carefully considered. These aspects include promoting species coexistence, suppressing noncontributors, choosing additional communities besides the highest functioning ones to reproduce, and reducing stochastic fluctuations in the biomass of each member species in Newborn communities. These considerations can be addressed experimentally. When executed effectively, community selection is predicted to improve costly community function, and may even force species to evolve slow growth to achieve species coexistence. Our conclusions hold under various alternative model assumptions and are therefore applicable to a variety of communities.

Microbial coexistence through chemical-mediated interactions

Many microbial functions happen within communities of interacting species. Explaining how species with disparate growth rates can coexist is important for applications such as manipulating host-associated microbiota or engineering industrial communities. Here, we ask how microbes interacting through their chemical environment can achieve coexistence in a continuous growth setup (similar to an industrial bioreactor or gut microbiota) where external resources are being supplied. We formulate and experimentally constrain a model in which mediators of interactions (e.g. metabolites or waste-products) are explicitly incorporated. Our model highlights facilitation and self-restraint as interactions that contribute to coexistence, consistent with our intuition. When interactions are strong, we observe that coexistence is determined primarily by the topology of facilitation and inhibition influences not their strengths. Importantly, we show that consumption or degradation of chemical mediators moderates interaction strengths and promotes coexistence. Our results offer insights into how to build or restructure microbial communities of interest.