The ecology and evolution of microbial metabolic strategies

Deciphering nutritional stress responses via knowledge-enriched transcriptomics for microbial engineering

Understanding diverse bacterial nutritional requirements and responses is foundational in microbial research and biotechnology. In this study, we employed knowledge-enriched transcriptomic analytics to decipher complex stress responses of Vibrio natriegens to supplied nutrients, aiming to enhance microbial engineering efforts. We computed 64 independently modulated gene sets that comprise a quantitative basis for transcriptome dynamics across a comprehensive transcriptomics dataset containing a broad array of nutrient conditions. Our approach led to the i) identification of novel transporter systems for diverse substrates, ii) a detailed understanding of how trace elements affect metabolism and growth, and iii) extensive characterization of nutrient-induced stress responses, including osmotic stress, low glycolytic flux, proteostasis, and altered protein expression. By clarifying the relationship between the acetate-associated regulon and glycolytic flux status of various nutrients, we have showcased its vital role in directing optimal carbon source selection. Our findings offer deep insights into the transcriptional landscape of bacterial nutrition and underscore its significance in tailoring strain engineering strategies, thereby facilitating the development of more efficient and robust microbial systems for biotechnological applications.

Proteome allocation and the evolution of metabolic cross-feeding

In a common instance of metabolic cross-feeding (MCF), an organism incompletely metabolizes nutrients and releases metabolites that are used by another to produce energy or building blocks. Why would the former waste edible food, and why does this preferentially occur at specific locations in a metabolic pathway have challenged evolutionary theory for decades. To address these questions, we combine adaptive dynamics with an explicit model of cell metabolism, including enzyme-driven catalysis of metabolic reactions and the cellular constraints acting on the proteome that may incur a cost to expressing all enzymes along a pathway. After pointing out that cells should in principle prioritize upstream reactions when metabolites are restrained inside the cell, we show that the occurrence of permeability-driven MCF is rare and requires that an intermediate metabolite be extremely diffusive. Indeed, only at very high levels of membrane permeability (consistent with those of acetate and glycerol, for instance) and under distinctive sets of parameters should the population diversify and MCF evolve. These results help understand the origins of simple microbial communities, such as those that readily evolve in short-term evolutionary experiments, and may later be extended to investigate how evolution has progressively built up today's extremely diverse ecosystems.

Leaf carbon chemistry effectively manipulated soil microbial profiles and induced metabolic adjustments under different revegetation types in the loess Plateau, China

Microorganisms are essential components of underground life systems and drive elemental cycling between plants and soil. Yet, in the ecologically fragile Loess Plateau, scant attention has been paid to the response of microbial communities to organic carbon (C) chemistry of both leaves and soils under different revegetation conditions, as well as subsequent alternation in their C metabolic functions. Here, Fourier transform infrared (FTIR) spectrum, amplicon sequencing of 16S rRNA and ITS, and temporal incubation with Biolog-Eco 96 plates were combined to explore the vegetative heterogeneity of microbial community properties and metabolic functions, as well as their regulatory mechanisms in three typical revegetation types including Robinia pseudoacacia L. (RF), Caragana korshinskii KOM. (SL), and abandoned grassland (AG). We observed higher bacterial-to-fungal ratios (B: F = 270.18) and richer copiotrophic bacteria (Proteobacteria = 33.08%) in RF soil than those in AG soil, suggesting that microbes were dominated by r-strategists in soil under RF treatment, which is mainly related to long-term priming of highly bioavailable leaf C (higher proportion of aromatic and hydrophilic functional groups and lower hydrophobicity). Conversely, microbial taxa in AG soil, which was characterized by higher leaf organic C hydrophobicity (1.39), were dominated by relatively more abundant fungi (lower B: F ratio = 149.49) and oligotrophic bacteria (Actinobacteria = 29.30%). The co-occurrence network analysis showed that microbial interactive associations in RF and AG soil were more complex and connective than in SL soil. Furthermore, Biolog-Eco plate experiments revealed that microorganisms tended to utilize labile C compounds (carbohydrates and amino acids) in RF soil and resistant C compounds (polymers) in AG soil, which were consistent with the substrate adaptation strategies of predominant microbial trophic groups in different revegetation environments. Meanwhile, we observed greater microbial metabolic activity and diversity advantages in RF vegetation. Collectively, we suggest that besides the nutrient variables in the leaf-soil system, the long-term regulation of the microbial community by the C chemistry of the leaf sequentially alters the microbial metabolic profiles in a domino-like manner. RF afforestation is more conducive to restoring soil microbial fertility (including microbial abundance, diversity, interactive association, and metabolic capacity). Our study potentially paves the way for achieving well-managed soil health and accurate prediction of C pool dynamics in areas undergoing ecological restoration of the Loess Plateau.

The optimization of microbial functions through rational environmental manipulations

Microorganisms play a central role in biotechnology and it is key that we develop strategies to engineer and optimize their functionality. To this end, most efforts have focused on introducing genetic manipulations in microorganisms which are then grown either in monoculture or in mixed-species consortia. An alternative strategy to optimize microbial processes is to rationally engineer the environment in which microbes grow. The microbial environment is multidimensional, including factors such as temperature, pH, salinity, nutrient composition, etc. These environmental factors all influence the growth and phenotypes of microorganisms and they generally "interact" with one another, combining their effects in complex, non-additive ways. In this piece, we overview the origins and consequences of these "interactions" between environmental factors and discuss how they have been built into statistical, bottom-up predictive models of microbial function to identify optimal environmental conditions for monocultures and microbial consortia. We also overview alternative "top-down" approaches, such as genetic algorithms, to finding optimal combinations of environmental factors. By providing a brief summary of the state of this field, we hope to stimulate further work on the rational manipulation and optimization of the microbial environment.

Geometry of ecological coexistence and niche differentiation

A fundamental problem in ecology is to understand how competition shapes biodiversity and species coexistence. Historically, one important approach for addressing this question has been to analyze consumer resource models using geometric arguments. This has led to broadly applicable principles such as Tilman's R^{*} and species coexistence cones. Here, we extend these arguments by constructing a geometric framework for understanding species coexistence based on convex polytopes in the space of consumer preferences. We show how the geometry of consumer preferences can be used to predict species which may coexist and enumerate ecologically stable steady states and transitions between them. Collectively, these results provide a framework for understanding the role of species traits within niche theory.

The Architecture of Metabolic Networks Constrains the Evolution of Microbial Resource Hierarchies

Microbial strategies for resource use are an essential determinant of their fitness in complex habitats. When facing environments with multiple nutrients, microbes often use them sequentially according to a preference hierarchy, resulting in well-known patterns of diauxic growth. In theory, the evolutionary diversification of metabolic hierarchies could represent a mechanism supporting coexistence and biodiversity by enabling temporal segregation of niches. Despite this ecologically critical role, the extent to which substrate preference hierarchies can evolve and diversify remains largely unexplored. Here, we used genome-scale metabolic modeling to systematically explore the evolution of metabolic hierarchies across a vast space of metabolic network genotypes. We find that only a limited number of metabolic hierarchies can readily evolve, corresponding to the most commonly observed hierarchies in genome-derived models. We further show how the evolution of novel hierarchies is constrained by the architecture of central metabolism, which determines both the propensity to change ranks between pairs of substrates and the effect of specific reactions on hierarchy evolution. Our analysis sheds light on the genetic and mechanistic determinants of microbial metabolic hierarchies, opening new research avenues to understand their evolution, evolvability, and ecology.

Geometry of ecological coexistence and niche differentiation

A fundamental problem in ecology is to understand how competition shapes biodiversity and species coexistence. Historically, one important approach for addressing this question has been to analyze Consumer Resource Models (CRMs) using geometric arguments. This has led to broadly applicable principles such as Tilman's R* and species coexistence cones. Here, we extend these arguments by constructing a novel geometric framework for understanding species coexistence based on convex polytopes in the space of consumer preferences. We show how the geometry of consumer preferences can be used to predict species coexistence and enumerate ecologically-stable steady states and transitions between them. Collectively, these results constitute a qualitatively new way of understanding the role of species traits in shaping ecosystems within niche theory.

Genome-resolved metagenomics reveals depth-related patterns of microbial community structure and functions in a highly stratified, AMD overlaying mine tailings

Acid mine drainage (AMD) is a worldwide environmental problem, yet bioremediation is hampered by a limited knowledge of the reductive microbial processes in the AMD ecosystem. Here, we generate extensive metagenome and geochemical datasets to investigate how microbial populations and metabolic capacities driving major element cycles are structured in a highly stratified, AMD overlaying tailings environment. The results demonstrated an explicit depth-dependent differentiation of microbial community composition and function profiles between the surface and deeper tailings layers, paralleling the dramatic shifts in major physical and geochemical properties. Specifically, key genes involved in sulfur and iron oxidation were significantly enriched in the surface tailings, whereas those associated with reductive nitrogen, sulfur, and iron processes were enriched in the deeper layers. Genome-resolved metagenomics retrieved 406 intermediate or high-quality genomes spanning 26 phyla, including major new groups (e.g., Patescibacteria and DPANN). Metabolic models involving nitrogen, sulfur, iron, and carbon cycles were proposed based on the functional potentials of the abundant microbial genomes, emphasizing syntrophy and the importance of lesser-known taxa in the degradation of complex carbon compounds. These results have implications for in situ AMD bioremediation.

Gut Microbiome Associating with Carbon and Nitrogen Metabolism during Biodegradation of Polyethene in Tenebrio larvae with Crop Residues as Co-Diets

Tenebrio molitor and Tenebrio obscurus (Coleoptera: Tenebrionidae) larvae are two commercial insects that eat plant and crop residues as diets and also biodegrade synthetic plastics polyethylene (PE). We examined biodegradation of low-density PE (LDPE) foam (M_n = 28.9 kDa and M_w = 342.0 kDa) with and without respective co-diets, i.e., wheat brain (WB) or corn flour (CF), corn straw (CS), and rice straw (RS) at 4:1 (w/w), and their gut microbiome and genetic metabolic functional groups at 27.0 ± 0.5 °C after 28 days of incubation. The presence of co-diets enhanced LDPE consumption in both larvae and broad-depolymerized the ingested LDPE. The diet type shaped gut microbial diversity, potential pathways, and metabolic functions. The sequence of effectiveness of co-diets was WB or CF > CS > RS for larval development and LDPE degradation. Co-occurrence networks indicated that the larvae co-fed with LDPE displayed more complex correlations of gut microbiome than the larvae fed with single diets. The primary diet of WB or CF and crop residues CS and RS provided energy and nitrogen source to significantly enhance LDPE biodegradation with synergistic activities of the gut microbiota. For the larvae fed LDPE and LDPE plus co-diets, nitrogen fixation function was stimulated compared to normal diets and associated with LDPE biodegradation.

Impact of Molasses on Ruminal Volatile Fatty Acid Production and Microbiota Composition In Vitro

The aim of this study was to assess if molasses could modify VFA production and the rumen microbial community in vitro. Three beet (treatment Beet) and three cane (treatment Cane) molasses preparations were randomly selected from a variety of samples collected worldwide and incubated in vitro with rumen fluid along with a control sample (treatment CTR, in which no molasses was used). Flasks for VFA analysis were sampled at 0, 1, 2, 3, 4, 6, 8, and 24 h of each incubation. For microbiota analysis, samples from each fermentation flask after 12 and 24 h were subjected to microbial DNA extraction and V3-V4 16S rRNA gene sequencing on an Illumina MiSeq platform. Total net VFA production was higher in the beet and cane preparations than in the control (CTR) group at 24 h (33 mmol/L, 34 mmol/L, and 24.8 mmol/L, respectively), and the composition of VFAs was affected by the inclusion of molasses: acetic acid increased in the CTR group (73.5 mol%), while propionic acid increased in the beet and cane molasses (19.6 mol% and 18.6 mol%, respectively), and butyric acid increased, especially in the cane group (23.2 mol%). Molasses even influenced the composition of the rumen microbiota, and particularly the relative abundance of the most dominant family in the rumen, Prevotellaceae, which decreased compared to CTR (37.13%, 28.88%, and 49.6%, respectively). In contrast, Streptococcaceae (19.62% and 28.10% in molasses compared to 6.23% in CTR), Veillonellaceae (6.48% and 8.67% in molasses compared to 4.54% in CTR), and Fibrobacteraceae (0.90% and 0.88% in molasses compared to 0.62% in CTR) increased in the beet and cane groups compared to the CTR group. Another important finding is the lower proportion of Methanobacteriaceae following the addition of molasses compared to CTR (0.26%, 0.28%, and 0.43%, respectively). This study showed the impact of molasses in influencing VFA production and composition as a result of a modified rumen microbial composition.

The acetoin assimilation pathway of Pseudomonas putida KT2440 is regulated by overlapping global regulatory elements that respond to nutritional cues

Many microorganisms produce and excrete acetoin (3-hydroxy-2-butanone) when growing in environments that contain glucose or other fermentable carbon sources. This excreted compound can then be assimilated by other bacterial species such as pseudomonads. This work shows that acetoin is not a preferred carbon source of Pseudomonas putida, and that the induction of genes required for its assimilation is down-modulated by different, independent, global regulatory systems when succinate, glucose or components of the LB medium are also present. The expression of the acetoin degradation genes was found to rely on the RpoN alternative sigma factor and to be modulated by the Crc/Hfq, Cyo and PTS^Ntr regulatory elements, with the impact of the latter three varying according to the carbon source present in addition to acetoin. Pyruvate, a poor carbon source for P. putida, did not repress acetoin assimilation. Indeed, the presence of acetoin significantly improved growth on pyruvate, revealing these compounds to have a synergistic effect. This would provide a clear competitive advantage to P. putida when growing in environments in which all the preferred carbon sources have been depleted and pyruvate and acetoin remain as leftovers from the fermentation of sugars by other microorganisms.

A Two-Compartment Fermentation System to Quantify Strain-Specific Interactions in Microbial Co-Cultures

To fulfil the growing interest in investigating microbial interactions in co-cultures, a novel two-compartment bioreactor system was developed, characterised, and implemented. The system allowed for the exchange of amino acids and peptides via a polyethersulfone membrane that retained biomass. Further system characterisation revealed a Bodenstein number of 18, which hints at backmixing. Together with other physical settings, the existence of unwanted inner-compartment substrate gradients could be ruled out. Furthermore, the study of Damkoehler numbers indicated that a proper metabolite supply between compartments was enabled. Implementing the two-compartment system (2cs) for growing Streptococcus thermophilus and Lactobacillus delbrueckii subs. bulgaricus, which are microorganisms commonly used in yogurt starter cultures, revealed only a small variance between the one-compartment and two-compartment approaches. The 2cs enabled the quantification of the strain-specific production and consumption rates of amino acids in an interacting S. thermophilus-L. bulgaricus co-culture. Therefore, comparisons between mono- and co-culture performance could be achieved. Both species produce and release amino acids. Only alanine was produced de novo from glucose through potential transaminase activity by L. bulgaricus and consumed by S. thermophilus. Arginine availability in peptides was limited to S. thermophilus' growth, indicating active biosynthesis and dependency on the proteolytic activity of L. bulgaricus. The application of the 2cs not only opens the door for the quantification of exchange fluxes between microbes but also enables continuous production modes, for example, for targeted evolution studies.

Better under stress: Improving bacterial cellulose production by Komagataeibacter xylinus K2G30 (UMCC 2756) using adaptive laboratory evolution

Among naturally produced polymers, bacterial cellulose is receiving enormous attention due to remarkable properties, making it suitable for a wide range of industrial applications. However, the low yield, the instability of microbial strains and the limited knowledge of the mechanisms regulating the metabolism of producer strains, limit the large-scale production of bacterial cellulose. In this study, Komagataeibacter xylinus K2G30 was adapted in mannitol based medium, a carbon source that is also available in agri-food wastes. K. xylinus K2G30 was continuously cultured by replacing glucose with mannitol (2% w/v) for 210 days. After a starting lag-phase, in which no changes were observed in the utilization of mannitol and in bacterial cellulose production (cycles 1-25), a constant improvement of the phenotypic performances was observed from cycle 26 to cycle 30, accompanied by an increase in mannitol consumption. At cycle 30, the end-point of the experiment, bacterial cellulose yield increased by 38% in comparision compared to cycle 1. Furthermore, considering the mannitol metabolic pathway, D-fructose is an intermediate in the bioconversion of mannitol to glucose. Based on this consideration, K. xylinus K2G30 was tested in fructose-based medium, obtaining the same trend of bacterial cellulose production observed in mannitol medium. The adaptive laboratory evolution approach used in this study was suitable for the phenotypic improvement of K. xylinus K2G30 in bacterial cellulose production. Metabolic versatility of the strain was confirmed by the increase in bacterial cellulose production from D-fructose-based medium. Moreover, the adaptation on mannitol did not occur at the expense of glucose, confirming the versatility of K2G30 in producing bacterial cellulose from different carbon sources. Results of this study contribute to the knowledge for designing new strategies, as an alternative to the genetic engineering approach, for bacterial cellulose production.

Dynamic proteome allocation regulates the profile of interaction of auxotrophic bacterial consortia

Microbial ecosystems are composed of multiple species in constant metabolic exchange. A pervasive interaction in microbial communities is metabolic cross-feeding and occurs when the metabolic burden of producing costly metabolites is distributed between community members, in some cases for the benefit of all interacting partners. In particular, amino acid auxotrophies generate obligate metabolic inter-dependencies in mixed populations and have been shown to produce a dynamic profile of interaction that depends upon nutrient availability. However, identifying the key components that determine the pair-wise interaction profile remains a challenging problem, partly because metabolic exchange has consequences on multiple levels, from allocating proteomic resources at a cellular level to modulating the structure, function and stability of microbial communities. To evaluate how ppGpp-mediated resource allocation drives the population-level profile of interaction, here we postulate a multi-scale mathematical model that incorporates dynamics of proteome partition into a population dynamics model. We compare our computational results with experimental data obtained from co-cultures of auxotrophic Escherichia coli K12 strains under a range of amino acid concentrations and population structures. We conclude by arguing that the stringent response promotes cooperation by inhibiting the growth of fast-growing strains and promoting the synthesis of metabolites essential for other community members.

Metabolic cross-feeding structures the assembly of polysaccharide degrading communities

Metabolic processes that fuel the growth of heterotrophic microbial communities are initiated by specialized biopolymer degraders that decompose complex forms of organic matter. It is unclear, however, to what extent degraders structure the downstream assembly of the community that follows polymer breakdown. Investigating a model marine microbial community that degrades chitin, we show that chitinases secreted by different degraders produce oligomers of specific chain lengths that not only select for specialized consumers but also influence the metabolites secreted by these consumers into a shared resource pool. Each species participating in the breakdown cascade exhibits unique hierarchical preferences for substrates, which underlies the sequential colonization of metabolically distinct groups as resource availability changes over time. By identifying the metabolic underpinnings of microbial community assembly, we reveal a hierarchical cross-feeding structure that allows biopolymer degraders to shape the dynamics of community assembly.

Resource-allocation constraint governs structure and function of microbial communities in metabolic modeling

Predictive modeling tools for assessing microbial communities are important for realizing transformative capabilities of microbiomes in agriculture, ecology, and medicine. Constraint-based community-scale metabolic modeling is unique in its potential for making mechanistic predictions regarding both the structure and function of microbial communities. However, accessing this potential requires an understanding of key physicochemical constraints, which are typically considered on a per-species basis. What is needed is a means of incorporating global constraints relevant to microbial ecology into community models. Resource-allocation constraint, which describes how limited resources should be distributed to different cellular processes, sets limits on the efficiency of metabolic and ecological processes. In this study, we investigate the implications of resource-allocation constraints in community-scale metabolic modeling through a simple mechanism-agnostic implementation of resource-allocation constraints directly at the flux level. By systematically performing single-, two-, and multi-species growth simulations, we show that resource-allocation constraints are indispensable for predicting the structure and function of microbial communities. Our findings call for a scalable workflow for implementing a mechanistic version of resource-allocation constraints to ultimately harness the full potential of community-scale metabolic modeling tools.

Why Do Antibiotics Exist?

In the struggle with antibiotic resistance, we are losing. There is now a serious threat of moving into a postantibiotic world. High levels of resistance, in terms of both frequency and strength, have evolved against all clinically approved antibiotics worldwide. The usable life span of new clinically approved antibiotics is typically less than a decade before resistance reaches frequencies so high as to require only guarded usage. However, microbes have produced antibiotics for millennia without resistance becoming an existential issue. If resistance is the inevitable consequence of antibiotic usage, as has been the human experience, why has it not become an issue for microbes as well, especially since resistance genes are as prevalent in nature as the genes responsible for antibiotic production? Here, we ask how antibiotics can exist given the almost ubiquitous presence of resistance genes in the very microbes that have produced and used antibiotics since before humans walked the planet. We find that the context of both production and usage of antibiotics by microbes may be key to understanding how resistance is managed over time, with antibiotic synthesis and resistance existing in a paired relationship, much like a cipher and key, that impacts microbial community assembly. Finally, we put forward the cohesive, ecologically based "secret society" hypothesis to explain the longevity of antibiotics in nature.

Enzyme regulation and mutation in a model serial-dilution ecosystem

Microbial communities are ubiquitous in nature and come in a multitude of forms, ranging from communities dominated by a handful of species to communities containing a wide variety of metabolically distinct organisms. This huge range in diversity is not a curiosity-microbial diversity has been linked to outcomes of substantial ecological and medical importance. However, the mechanisms underlying microbial diversity are still under debate, as simple mathematical models only permit as many species to coexist as there are resources. A plethora of mechanisms have been proposed to explain the origins of microbial diversity, but many of these analyses omit a key property of real microbial ecosystems: the propensity of the microbes themselves to change their growth properties within and across generations. In order to explore the impact of this key property on microbial diversity, we expand upon a recently developed model of microbial diversity in fluctuating environments. We implement changes in growth strategy in two distinct ways. First, we consider the regulation of a cell's enzyme levels within short, ecological times, and second we consider evolutionary changes driven by mutations across generations. Interestingly, we find that these two types of microbial responses to the environment can have drastically different outcomes. Enzyme regulation may collapse diversity over long enough times while, conversely, strategy-randomizing mutations can produce a "rich-get-poorer" effect that promotes diversity. This paper makes explicit, using a simple serial-dilutions framework, the conflicting ways that microbial adaptation and evolution can affect community diversity.

Resource-diversity relationships in bacterial communities reflect the network structure of microbial metabolism

The relationship between the number of available nutrients and community diversity is a central question in ecological research that remains unanswered. Here we studied the assembly of hundreds of soil-derived microbial communities on a wide range of well-defined resource environments, from single carbon sources to combinations of up to 16. We found that, while single resources supported multispecies communities varying from 8 to 40 taxa, mean community richness increased only one-by-one with additional resources. Cross-feeding could reconcile these seemingly contrasting observations, with the metabolic network seeded by the supplied resources explaining the changes in richness due to both the identity and the number of resources, as well as the distribution of taxa across different communities. By using a consumer-resource model incorporating the inferred cross-feeding network, we provide further theoretical support to our observations and a framework to link the type and number of environmental resources to microbial community diversity.

Constrained proteome allocation affects coexistence in models of competitive microbial communities

Microbial communities are ubiquitous and play crucial roles in many natural processes. Despite their importance for the environment, industry and human health, there are still many aspects of microbial community dynamics that we do not understand quantitatively. Recent experiments have shown that the structure and composition of microbial communities are intertwined with the metabolism of the species that inhabit them, suggesting that properties at the intracellular level such as the allocation of cellular proteomic resources must be taken into account when describing microbial communities with a population dynamics approach. In this work, we reconsider one of the theoretical frameworks most commonly used to model population dynamics in competitive ecosystems, MacArthur's consumer-resource model, in light of experimental evidence showing how proteome allocation affects microbial growth. This new framework allows us to describe community dynamics at an intermediate level of complexity between classical consumer-resource models and biochemical models of microbial metabolism, accounting for temporally-varying proteome allocation subject to constraints on growth and protein synthesis in the presence of multiple resources, while preserving analytical insight into the dynamics of the system. We first show with a simple experiment that proteome allocation needs to be accounted for to properly understand the dynamics of even the simplest microbial community, i.e. two bacterial strains competing for one common resource. Then, we study our consumer-proteome-resource model analytically and numerically to determine the conditions that allow multiple species to coexist in systems with arbitrary numbers of species and resources.

Nutrient dominance governs the assembly of microbial communities in mixed nutrient environments

A major open question in microbial community ecology is whether we can predict how the components of a diet collectively determine the taxonomic composition of microbial communities. Motivated by this challenge, we investigate whether communities assembled in pairs of nutrients can be predicted from those assembled in every single nutrient alone. We find that although the null, naturally additive model generally predicts well the family-level community composition, there exist systematic deviations from the additive predictions that reflect generic patterns of nutrient dominance at the family level. Pairs of more-similar nutrients (e.g. two sugars) are on average more additive than pairs of more dissimilar nutrients (one sugar-one organic acid). Furthermore, sugar-acid communities are generally more similar to the sugar than the acid community, which may be explained by family-level asymmetries in nutrient benefits. Overall, our results suggest that regularities in how nutrients interact may help predict community responses to dietary changes.

Ignoring social distancing: advances in understanding multi-species bacterial interactions

Almost every ecosystem on this planet is teeming with microbial communities made of diverse bacterial species. At a reductionist view, many of these bacteria form pairwise interactions, but, as the field of view expands, the neighboring organisms and the abiotic environment can play a crucial role in shaping the interactions between species. Over the years, a strong foundation of knowledge has been built on isolated pairwise interactions between bacteria, but now the field is advancing toward understanding how cohabitating bacteria and natural surroundings affect these interactions. Use of bottom-up approaches, piecing communities together, and top-down approaches that deconstruct communities are providing insight on how different species interact. In this review, we highlight how studies are incorporating more complex communities, mimicking the natural environment, and recurring findings such as the importance of cooperation for stability in harsh environments and the impact of bacteria-induced environmental pH shifts. Additionally, we will discuss how omics are being used as a top-down approach to identify previously unknown interspecies bacterial interactions and the challenges of these types of studies for microbial ecology.

Modeling microbial metabolic trade-offs in a chemostat

Microbes face intense competition in the natural world, and so need to wisely allocate their resources to multiple functions, in particular to metabolism. Understanding competition among metabolic strategies that are subject to trade-offs is therefore crucial for deeper insight into the competition, cooperation, and community assembly of microorganisms. In this work, we evaluate competing metabolic strategies within an ecological context by considering not only how the environment influences cell growth, but also how microbes shape their chemical environment. Utilizing chemostat-based resource-competition models, we exhibit a set of intuitive and general procedures for assessing metabolic strategies. Using this framework, we are able to relate and unify multiple metabolic models, and to demonstrate how the fitness landscape of strategies becomes intrinsically dynamic due to species-environment feedback. Such dynamic fitness landscapes produce rich behaviors, and prove to be crucial for ecological and evolutionarily stable coexistence in all the models we examined.