Directed Evolution of Microbial Communities

Starting with screening strains to construct synthetic microbial communities (SynComs) for traditional food fermentation

With the elucidation of community structures and assembly mechanisms in various fermented foods, core communities that significantly influence or guide fermentation have been pinpointed and used for exogenous restructuring into synthetic microbial communities (SynComs). These SynComs simulate ecological systems or function as adjuncts or substitutes in starters, and their efficacy has been widely verified. However, screening and assembly are still the main limiting factors for implementing theoretic SynComs, as desired strains cannot be effectively obtained and integrated. To expand strain screening methods suitable for SynComs in food fermentation, this review summarizes the recent research trends in using SynComs to study community evolution or interaction and improve the quality of food fermentation, as well as the specific process of constructing synthetic communities. The potential for novel screening modalities based on genes, enzymes and metabolites in food microbial screening is discussed, along with the emphasis on strategies to optimize assembly for facilitating the development of synthetic communities.

Evolution and stability of complex microbial communities driven by trade-offs

Microbial communities are ubiquitous in nature and play an important role in ecology and human health. Cross-feeding is thought to be core to microbial communities, though it remains unclear precisely why it emerges. Why have multi-species microbial communities evolved in many contexts and what protects microbial consortia from invasion? Here, we review recent insights into the emergence and stability of coexistence in microbial communities. A particular focus is the long-term evolutionary stability of coexistence, as observed for microbial communities that spontaneously evolved in the E. coli long-term evolution experiment (LTEE). We analyze these findings in the context of recent work on trade-offs between competing microbial objectives, which can constitute a mechanistic basis for the emergence of coexistence. Coexisting communities, rather than monocultures of the 'fittest' single strain, can form stable endpoints of evolutionary trajectories. Hence, the emergence of coexistence might be an obligatory outcome in the evolution of microbial communities. This implies that rather than embodying fragile metastable configurations, some microbial communities can constitute formidable ecosystems that are difficult to disrupt.

Metabolic complexity drives divergence in microbial communities

Microbial communities are shaped by environmental metabolites, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. Here we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but they diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively simpler ones that then participate in cross-feeding between community members, is necessary and sufficient to recapitulate our experimental observations. In addition to helping understand the role of the environment in community assembly, the divergence-complexity effect can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions towards microbiome engineering applications.

Synthetic microbial ecology: engineering habitats for modular consortia

Microbiomes, the complex networks of micro-organisms and the molecules through which they interact, play a crucial role in health and ecology. Over at least the past two decades, engineering biology has made significant progress, impacting the bio-based industry, health, and environmental sectors; but has only recently begun to explore the engineering of microbial ecosystems. The creation of synthetic microbial communities presents opportunities to help us understand the dynamics of wild ecosystems, learn how to manipulate and interact with existing microbiomes for therapeutic and other purposes, and to create entirely new microbial communities capable of undertaking tasks for industrial biology. Here, we describe how synthetic ecosystems can be constructed and controlled, focusing on how the available methods and interaction mechanisms facilitate the regulation of community composition and output. While experimental decisions are dictated by intended applications, the vast number of tools available suggests great opportunity for researchers to develop a diverse array of novel microbial ecosystems.

Global WWTP Microbiome-based Integrative Information Platform: From experience to intelligence

Domestic and industrial wastewater treatment plants (WWTPs) are facing formidable challenges in effectively eliminating emerging pollutants and conventional nutrients. In microbiome engineering, two approaches have been developed: a top-down method focusing on domesticating seed microbiomes into engineered ones, and a bottom-up strategy that synthesizes engineered microbiomes from microbial isolates. However, these approaches face substantial hurdles that limit their real-world applicability in wastewater treatment engineering. Addressing this gap, we propose the creation of a Global WWTP Microbiome-based Integrative Information Platform, inspired by the untapped microbiome and engineering data from WWTPs and advancements in artificial intelligence (AI). This open platform integrates microbiome and engineering information globally and utilizes AI-driven tools for identifying seed microbiomes for new plants, providing technical upgrades for existing facilities, and deploying microbiomes for accidental pollution remediation. Beyond its practical applications, this platform has significant scientific and social value, supporting multidisciplinary research, documenting microbial evolution, advancing Wastewater-Based Epidemiology, and enhancing global resource sharing. Overall, the platform is expected to enhance WWTPs' performance in pollution control, safeguarding a harmonious and healthy future for human society and the natural environment.

Rhizosphere engineering for soil carbon sequestration

The rhizosphere is the central hotspot of water and nutrient uptake by plants, rhizodeposition, microbial activities, and plant-soil-microbial interactions. The plasticity of plants offers possibilities to engineer the rhizosphere to mitigate climate change. We define rhizosphere engineering as targeted manipulation of plants, soil, microorganisms, and management to shift rhizosphere processes for specific aims [e.g., carbon (C) sequestration]. The rhizosphere components can be engineered by agronomic, physical, chemical, biological, and genomic approaches. These approaches increase plant productivity with a special focus on C inputs belowground, increase microbial necromass production, protect organic compounds and necromass by aggregation, and decrease C losses. Finally, we outline multifunctional options for rhizosphere engineering: how to boost C sequestration, increase soil health, and mitigate global change effects.

Enhancing phosphate-solubilising microbial communities through artificial selection

Microbial communities, acting as key drivers of ecosystem processes, harbour immense potential for sustainable agriculture practices. Phosphate-solubilising microorganisms, for example, can partially replace conventional phosphate fertilisers, which rely on finite resources. However, understanding the mechanisms and engineering efficient communities poses a significant challenge. In this study, we employ two artificial selection methods, environmental perturbation, and propagation, to construct phosphate-solubilising microbial communities. To assess trait transferability, we investigate the community performance in different media and a hydroponic system with Chrysanthemum indicum. Our findings reveal a distinct subset of phosphate-solubilising bacteria primarily dominated by Klebsiella and Enterobacterales. The propagated communities consistently demonstrate elevated levels of phosphate solubilisation, surpassing the starting soil community by 24.2% in activity. The increased activity of propagated communities remains consistent upon introduction into the hydroponic system. This study shows the efficacy of community-level artificial selection, particularly through propagation, as a tool for successfully modifying microbial communities to enhance phosphate solubilisation.

Serial cultures in invert emulsion and monophase systems for microbial community shaping and propagation

BACKGROUND

Microbial communities harbor important biotechnological potential in diverse domains, however, the engineering and propagation of such communities still face both knowledge and know-how gaps. More specifically, culturing tools are needed to propagate and shape microbial communities, to obtain desired properties, and to exploit them. Previous work suggested that micro-confinement and segregation of microorganisms using invert (water-in-oil, w/o) emulsion broth can shape communities during propagation, by alleviating biotic interactions and inducing physiological changes in cultured bacteria. The present work aimed at evaluating invert emulsion and simple broth monophasic cultures for the propagation and shaping of bacterial communities derived from raw milk in a serial propagation design.

RESULTS

The monophasic setup resulted in stable community structures during serial propagation, whereas the invert emulsion system resulted in only transiently stable structures. In addition, different communities with different taxonomic compositions could be obtained from a single inoculum. Furthermore, the implementation of invert emulsion systems has allowed for the enrichment of less abundant microorganisms and consequently facilitated their isolation on culture agar plates.

CONCLUSIONS

The monophasic system enables communities to be propagated in a stable manner, whereas the invert emulsion system allowed for the isolation of less abundant microorganisms and the generation of diverse taxonomic compositions from a single inoculum.

Artificial selection of microbial communities: what have we learnt and how can we improve?

Microbial communities are capable of performing diverse functions with important bioindustrial and medical applications. One approach to improving community function is to breed new communities by artificially selecting for those displaying high community function ('community selection'). Importantly, community selection can improve the function of interest without needing to understand how the function arises, just like in classical artificial selection of individuals. However, experimental studies of community selection have had varied and largely limited success. Here, we review a conceptual framework to help foster an understanding of community selection and its associated challenges, and provide broad insights for designing effective selection strategies.

Strategies for tailoring functional microbial synthetic communities

Natural ecosystems harbor a huge reservoir of taxonomically diverse microbes that are important for plant growth and health. The vast diversity of soil microorganisms and their complex interactions make it challenging to pinpoint the main players important for the life support functions microbes can provide to plants, including enhanced tolerance to (a)biotic stress factors. Designing simplified microbial synthetic communities (SynComs) helps reduce this complexity to unravel the molecular and chemical basis and interplay of specific microbiome functions. While SynComs have been successfully employed to dissect microbial interactions or reproduce microbiome-associated phenotypes, the assembly and reconstitution of these communities have often been based on generic abundance patterns or taxonomic identities and co-occurrences but have only rarely been informed by functional traits. Here, we review recent studies on designing functional SynComs to reveal common principles and discuss multidimensional approaches for community design. We propose a strategy for tailoring the design of functional SynComs based on integration of high-throughput experimental assays with microbial strains and computational genomic analyses of their functional capabilities.

Conspecific versus heterospecific transmission shapes host specialization of the phyllosphere microbiome

In disease ecology, pathogen transmission among conspecific versus heterospecific hosts is known to shape pathogen specialization and virulence, but we do not yet know if similar effects occur at the microbiome level. We tested this idea by experimentally passaging leaf-associated microbiomes either within conspecific or across heterospecific plant hosts. Although conspecific transmission results in persistent host-filtering effects and more within-microbiome network connections, heterospecific transmission results in weaker host-filtering effects but higher levels of interconnectivity. When transplanted onto novel plants, heterospecific lines are less differentiated by host species than conspecific lines, suggesting a shift toward microbiome generalism. Finally, conspecific lines from tomato exhibit a competitive advantage on tomato hosts against those passaged on bean or pepper, suggesting microbiome-level host specialization. Overall, we find that transmission mode and previous host history shape microbiome diversity, with repeated conspecific transmission driving microbiome specialization and repeated heterospecific transmission promoting microbiome generalism.

Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes

Microbiome engineering offers the potential to leverage microbial communities to improve outcomes in human health, agriculture, and climate. To translate this potential into reality, it is crucial to reliably predict community composition and function. But a brute force approach to cataloging community function is hindered by the combinatorial explosion in the number of ways we can combine microbial species. An alternative is to parameterize microbial community outcomes using simplified, mechanistic models, and then extrapolate these models beyond where we have sampled. But these approaches remain data-hungry, as well as requiring an a priori specification of what kinds of mechanisms are included and which are omitted. Here, we resolve both issues by introducing a mechanism-agnostic approach to predicting microbial community compositions and functions using limited data. The critical step is the identification of a sparse representation of the community landscape. We then leverage this sparsity to predict community compositions and functions, drawing from techniques in compressive sensing. We validate this approach on in silico community data, generated from a theoretical model. By sampling just [Formula: see text]1% of all possible communities, we accurately predict community compositions out of sample. We then demonstrate the real-world application of our approach by applying it to four experimental datasets and showing that we can recover interpretable, accurate predictions on composition and community function from highly limited data.

The effectiveness of artificial microbial community selection: a conceptual framework and a meta-analysis

The potential for artificial selection at the community level to improve ecosystem functions has received much attention in applied microbiology. However, we do not yet understand what conditions in general allow for successful artificial community selection. Here we propose six hypotheses about factors that determine the effectiveness of artificial microbial community selection, based on previous studies in this field and those on multilevel selection. In particular, we emphasize selection strategies that increase the variance among communities. We then report a meta-analysis of published artificial microbial community selection experiments. The reported responses to community selection were highly variable among experiments; and the overall effect size was not significantly different from zero. The effectiveness of artificial community selection was greater when there was no migration among communities, and when the number of replicated communities subjected to selection was larger. The meta-analysis also suggests that the success of artificial community selection may be contingent on multiple necessary conditions. We argue that artificial community selection can be a promising approach, and suggest some strategies for improving the performance of artificial community selection programs.

Partner-assisted artificial selection of a secondary function for efficient bioremediation

Microbial enzymes can address diverse challenges such as degradation of toxins. However, if the function of interest does not confer a sufficient fitness effect on the producer, the enzymatic function cannot be improved in the host cells by a conventional selection scheme. To overcome this limitation, we propose an alternative scheme, termed "partner-assisted artificial selection" (PAAS), wherein the population of enzyme producers is assisted by function-dependent feedback from an accessory population. Simulations investigating the efficiency of toxin degradation reveal that this strategy supports selection of improved degradation performance, which is robust to stochasticity in the model parameters. We observe that conventional considerations still apply in PAAS: more restrictive bottlenecks lead to stronger selection but add uncertainty. Overall, we offer a guideline for successful implementation of PAAS and highlight its potentials and limitations.

Environmental selection and evolutionary process jointly shape genomic and functional profiles of mangrove rhizosphere microbiomes

Mangrove reforestation with introduced species has been an important strategy to restore mangrove ecosystem functioning. However, how such activities affect microbially driven methane (CH4), nitrogen (N), and sulfur (S) cycling of rhizosphere microbiomes remains unclear. To understand the effect of environmental selection and the evolutionary process on microbially driven biogeochemical cycles in native and introduced mangrove rhizospheres, we analyzed key genomic and functional profiles of rhizosphere microbiomes from native and introduced mangrove species by metagenome sequencing technologies. Compared with the native mangrove (Kandelia obovata, KO), the introduced mangrove (Sonneratia apetala, SA) rhizosphere microbiome had significantly (p < 0.05) higher average genome size (AGS) (5.8 vs. 5.5 Mb), average 16S ribosomal RNA gene copy number (3.5 vs. 3.1), relative abundances of mobile genetic elements, and functional diversity in terms of the Shannon index (7.88 vs. 7.84) but lower functional potentials involved in CH4 cycling (e.g., mcrABCDG and pmoABC), N2 fixation (nifHDK), and inorganic S cycling (dsrAB, dsrC, dsrMKJOP, soxB, sqr, and fccAB). Similar results were also observed from the recovered Proteobacterial metagenome-assembled genomes with a higher AGS and distinct functions in the introduced mangrove rhizosphere. Additionally, salinity and ammonium were identified as the main environmental drivers of functional profiles of mangrove rhizosphere microbiomes through deterministic processes. This study advances our understanding of microbially mediated biogeochemical cycling of CH4, N, and S in the mangrove rhizosphere and provides novel insights into the influence of environmental selection and evolutionary processes on ecosystem functions, which has important implications for future mangrove reforestation.

Artificial selection of communities drives the emergence of structured interactions

Species-rich communities, such as the microbiota or microbial ecosystems, provide key functions for human health and climatic resilience. Increasing effort is being dedicated to design experimental protocols for selecting community-level functions of interest. These experiments typically involve selection acting on populations of communities, each of which is composed of multiple species. If numerical simulations started to explore the evolutionary dynamics of this complex, multi-scale system, a comprehensive theoretical understanding of the process of artificial selection of communities is still lacking. Here, we propose a general model for the evolutionary dynamics of communities composed of a large number of interacting species, described by disordered generalised Lotka-Volterra equations. Our analytical and numerical results reveal that selection for scalar community functions leads to the emergence, along an evolutionary trajectory, of a low-dimensional structure in an initially featureless interaction matrix. Such structure reflects the combination of the properties of the ancestral community and of the selective pressure. Our analysis determines how the speed of adaptation scales with the system parameters and the abundance distribution of the evolved communities. Artificial selection for larger total abundance is thus shown to drive increased levels of mutualism and interaction diversity. Inference of the interaction matrix is proposed as a method to assess the emergence of structured interactions from experimentally accessible measures.

Metabolic complexity drives divergence in microbial communities

Microbial communities are shaped by the metabolites available in their environment, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. To this end, we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the diverse assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively smaller ones, is necessary and sufficient to recapitulate all of our experimental observations. In addition to pointing to a fundamental principle of community assembly, the divergence-complexity effect has important implications for microbiome engineering applications, as it can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions.

Pulsed, continuous or somewhere in between? Resource dynamics matter in the optimisation of microbial communities

The optimisation of synthetic and natural microbial communities has vast potential for emerging applications in medicine, agriculture and industry. Realising this goal is contingent on a close correlation between theory, experiments, and the real world. Although the temporal pattern of resource supply can play a major role in microbial community assembly, resource dynamics are commonly treated inconsistently in theoretical and experimental research. Here we explore how the composition of communities varies under continuous resource supply, typical of theoretical approaches, versus pulsed resource supply, typical of experiments. Using simulations of classical resource competition models, we show that community composition diverges rapidly between the two regimes, with almost zero overlap in composition once the pulsing interval stretches beyond just four hours. The implication for the rapidly growing field of microbial community optimisation is that the resource supply regime must be tailored to the community being optimised. As such, we argue that resource supply dynamics should be considered both a constraint in the design of novel microbial communities and as a tuning mechanism for the optimisation of pre-existing communities like those found in the human gut.

Microbial Drivers of Plant Performance during Drought Depend upon Community Composition and the Greater Soil Environment

The increasing occurrence of drought is a global challenge that threatens food security through direct impacts to both plants and their interacting soil microorganisms. Plant growth promoting microbes are increasingly being harnessed to improve plant performance under stress. However, the magnitude of microbiome impacts on both structural and physiological plant traits under water limited and water replete conditions are not well-characterized. Using two microbiomes sourced from a ponderosa pine forest and an agricultural field, we performed a greenhouse experiment that used a crossed design to test the individual and combined effects of the water availability and the soil microbiome composition on plant performance. Specifically, we studied the structural and leaf functional traits of maize that are relevant to drought tolerance. We further examined how microbial relationships with plant phenotypes varied under different combinations of microbial composition and water availability. We found that water availability and microbial composition affected plant structural traits. Surprisingly, they did not alter leaf function. Maize grown in the forest-soil microbiome produced larger plants under well-watered and water-limited conditions, compared to an agricultural soil community. Although leaf functional traits were not significantly different between the watering and microbiome treatments, the bacterial composition and abundance explained significant variability in both plant structure and leaf function within individual treatments, especially water-limited plants. Our results suggest that bacteria-plant interactions that promote plant performance under stress depend upon the greater community composition and the abiotic environment. IMPORTANCE Globally, drought is an increasingly common and severe stress that causes significant damage to agricultural and wild plants, thereby threatening food security. Despite growing evidence of the potential benefits of soil microorganisms on plant performance under stress, decoupling the effects of the microbiome composition versus the water availability on plant growth and performance remains a challenge. We used a highly controlled and replicated greenhouse experiment to understand the impacts of microbial community composition and water limitation on corn growth and drought-relevant functions. We found that both factors affected corn growth, and, interestingly, that individual microbial relationships with corn growth and leaf function were unique to specific watering/microbiome treatment combinations. This finding may help explain the inconsistent success of previously identified microbial inocula in improving plant performance in the face of drought, outside controlled environments.

Toxicity sharing model of earthworm intestinal microbiome reveals shared functional genes are more powerful than species in resisting pesticide stress

Earthworm intestinal bacteria and indigenous soil bacteria work closely during various biochemical processes and play a crucial role in maintaining the internal stability of the soil environment. However, the response mechanism of these bacterial communities to external pesticide disturbance is unknown. In this study, soil and earthworm gut contents were metagenomically sequenced after exposure to various concentrations of nitrochlorobenzene (0-1026.7 mg kg^-1). A high degree of similarity was found between the microbial community composition and abundance in the worm gut and soil, both of which decreased significantly (P < 0.05) under elevated pesticide stress. The toxicity sharing model (TSM) showed that the toxicity sharing capacity was 97.4-125.7 % and 100.4-130.2 % for E_genes (genes in the worm gut) and E_met(degradation genes in the worm gut) in the earthworm intestinal microbiome, respectively. This indicated that the earthworm intestinal microbiome assisted in relieving the pesticide toxicity of the indigenous soil microbiome. This study showed that the TSM could quantitatively describe the toxic effect of pesticides on the earthworm intestinal microbiome. It provides a new analytical model for investigating the ecological alliance between earthworm intestinal microbiome and indigenous soil microbiome under pesticide stress while contributing a more profound understanding of the potential to use earthworms to mitigate pesticide pollution in soils and develop earthworm-based soil remediation techniques.

Engineered Living Materials For Sustainability

Recent advances in synthetic biology and materials science have given rise to a new form of materials, namely engineered living materials (ELMs), which are composed of living matter or cell communities embedded in self-regenerating matrices of their own or artificial scaffolds. Like natural materials such as bone, wood, and skin, ELMs, which possess the functional capabilities of living organisms, can grow, self-organize, and self-repair when needed. They also spontaneously perform programmed biological functions upon sensing external cues. Currently, ELMs show promise for green energy production, bioremediation, disease treatment, and fabricating advanced smart materials. This review first introduces the dynamic features of natural living systems and their potential for developing novel materials. We then summarize the recent research progress on living materials and emerging design strategies from both synthetic biology and materials science perspectives. Finally, we discuss the positive impacts of living materials on promoting sustainability and key future research directions.

The community-function landscape of microbial consortia

Quantitatively linking the composition and function of microbial communities is a major aspiration of microbial ecology. Microbial community functions emerge from a complex web of molecular interactions between cells, which give rise to population-level interactions among strains and species. Incorporating this complexity into predictive models is highly challenging. Inspired by a similar problem in genetics of predicting quantitative phenotypes from genotypes, an ecological community-function (or structure-function) landscape could be defined that maps community composition and function. In this piece, we present an overview of our current understanding of these community landscapes, their uses, limitations, and open questions. We argue that exploiting the parallels between both landscapes could bring powerful predictive methodologies from evolution and genetics into ecology, providing a boost to our ability to engineer and optimize microbial consortia.

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.

Guided by the principles of microbiome engineering: Accomplishments and perspectives for environmental use

Although the accomplishments of microbiome engineering highlight its significance for the targeted manipulation of microbial communities, knowledge and technical gaps still limit the applications of microbiome engineering in biotechnology, especially for environmental use. Addressing the environmental challenges of refractory pollutants and fluctuating environmental conditions requires an adequate understanding of the theoretical achievements and practical applications of microbiome engineering. Here, we review recent cutting-edge studies on microbiome engineering strategies and their classical applications in bioremediation. Moreover, a framework is summarized for combining both top-down and bottom-up approaches in microbiome engineering toward improved applications. A strategy to engineer microbiomes for environmental use, which avoids the build-up of toxic intermediates that pose a risk to human health, is suggested. We anticipate that the highlighted framework and strategy will be beneficial for engineering microbiomes to address difficult environmental challenges such as degrading multiple refractory pollutants and sustain the performance of engineered microbiomes in situ with indigenous microorganisms under fluctuating conditions.

Modification of nitrile hydratase from Rhodococcus erythropolis CCM2595 by semirational design to enhance its substrate affinity

Nitrile hydratase (NHase, EC 4.2.1.84) is an excellent biocatalyst that catalyzes the hydration of nitrile substances to their corresponding amides. Given its catalytic specificity and eco-friendliness, NHase has extensive applications in the chemical, pharmaceutical, and cosmetic industries. To improve the affinity between Rhodococcus erythropolis CCM2595-derived NHase (ReNHase) and adiponitrile, this study used a semirational design to improve the efficiency of ReNHase in catalyzing the generation of 5-cyanopentanamide from adiponitrile. Enzyme kinetics analysis showed that Km of the mutant ReNHase^B:G196Y was 3.265 mmol l^-1, which was lower than that of the wild-type NHase. The affinity of the mutant ReNHase^B:G196Y to adiponitrile was increased by 36.35%, and the efficiency of the mutant ReNHase^B:G196Y in catalyzing adiponitrile to 5-cyanopentamide was increased by 10.11%. The analysis of the enzyme-substrate interaction showed that the hydrogen bond length of the mutant ReNHase^B:G196Y to adiponitrile was shortened by 0.59 Å, which enhanced the interaction between the mutant and adiponitrile and, thereby, increased the substrate affinity. Similarly, the structural analysis showed that the amino acid flexibility near the mutation site of ReNHase^B:G196Y was increased, which enhanced the binding force between the enzyme and adiponitrile. Our work may provide a new theoretical basis for the modification of substrate affinity of NHase and increase the possibility of industrial applications of the enzyme.

Iron-dependent mutualism between Chlorella sorokiniana and Ralstonia pickettii forms the basis for a sustainable bioremediation system

Phototrophic communities of autotrophic microalgae and heterotrophic bacteria perform complex tasks of nutrient acquisition and tackling environmental stress but remain underexplored as a basis for the bioremediation of emerging pollutants. In industrial monoculture designs, poor iron uptake by microalgae limits their productivity and biotechnological efficacy. Iron supplementation is expensive and ineffective because iron remains insoluble in an aqueous medium and is biologically unavailable. However, microalgae develop complex interkingdom associations with siderophore-producing bacteria that help solubilize iron and increase its bioavailability. Using dye degradation as a model, we combined environmental isolations and synthetic ecology as a workflow to design a simplified microbial community based on iron and carbon exchange. We established a mutualism between the previously non-associated alga Chlorella sorokiniana and siderophore-producing bacterium Ralstonia pickettii. Siderophore-mediated increase in iron bioavailability alleviated Fe stress for algae and increased the reductive iron uptake mechanism and bioremediation potential. In exchange, C. sorokiniana produced galactose, glucose, and mannose as major extracellular monosaccharides, supporting bacterial growth. We propose that extracellular iron reduction by ferrireductase is crucial for azoreductase-mediated dye degradation in microalgae. These results demonstrate that iron bioavailability, often overlooked in cultivation, governs microalgal growth, enzymatic processes, and bioremediation potential. Our results suggest that phototrophic communities with an active association for iron and carbon exchange have the potential to overcome challenges associated with micronutrient availability, while scaling up bioremediation designs.

Artificial selection methods from evolutionary computing show promise for directed evolution of microbes

Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a general-purpose search engine for solutions to challenging computational problems. Despite their overlapping approaches, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. In this work, we ask whether parent selection algorithms—procedures for choosing promising progenitors—from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we introduce an agent-based model of directed microbial evolution, which we used to evaluate how well three selection algorithms from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to methods commonly used in the laboratory (elite and top 10% selection). We found that multiobjective selection techniques from evolutionary computing (lexicase and non-dominated elite) generally outperformed the commonly used directed evolution approaches when selecting for multiple traits of interest. Our results motivate ongoing work transferring these multiobjective selection procedures into the laboratory and a continued evaluation of more sophisticated artificial selection methods.

Humans have long known how to co-opt evolutionary processes for their own benefit. Carefully choosing which individuals to breed so that beneficial traits would take hold, they have domesticated dogs, wheat, cows and many other species to fulfil their needs.

Biologists have recently refined these ‘artificial selection’ approaches to focus on microorganisms. The hope is to obtain microbes equipped with desirable features, such as the ability to degrade plastic or to produce valuable molecules. However, existing ways of using artificial selection on microbes are limited and sometimes not effective.

Computer scientists have also harnessed evolutionary principles for their own purposes, developing highly effective artificial selection protocols that are used to find solutions to challenging computational problems. Yet because of limited communication between the two fields, sophisticated selection protocols honed over decades in evolutionary computing have yet to be evaluated for use in biological populations.

In their work, Lalejini et al. compared popular artificial selection protocols developed for either evolutionary computing or work with microorganisms. Two computing selection methods showed promise for improving directed evolution in the laboratory. Crucially, these selection protocols differed from conventionally used methods by selecting for both diversity and performance, rather than performance alone. These promising approaches are now being tested in the laboratory, with potentially far-reaching benefits for medical, biotech, and agricultural applications.

While evolutionary computing owes its origins to our understanding of biological processes, it has much to offer in return to help us harness those same mechanisms. The results by Lalejini et al. help to bridge the gap between computational and biological communities who could both benefit from increased collaboration.

Community diversity determines the evolution of synthetic bacterial communities under artificial selection

Artificial selection can be conducted at the community level in the laboratory through a differential propagation of the communities according to their level of expression of a targeted function. Working with communities instead of individuals as selection units brings in additional sources of variation in the considered function that can influence the outcome of the artificial selection. In this study, we wanted to assess the effect of manipulating the initial community richness on artificial selection efficiency, defined as the change in the targeted function over time. We applied artificial selection for a high productivity on synthetic bacterial communities varying for their richness (from one to 16 strains). Overall, the selected communities were 16% more productive than the control communities, but a convergence of community composition might have limited the effect of diversity on artificial selection efficiency. Community richness positively influenced community productivity and metabolic capacities and was a strong determinant of the dynamics of community evolution. We propose that applying artificial selection on communities varying for their diversity could be a way to find communities differing for their level of expression of a function but also for their responsiveness to artificial selection, provided that their initial composition is different enough.

Microbial experimental evolution in a massively multiplexed and high-throughput era

Experimental evolution with microbial model systems has transformed our understanding of the basic rules underlying ecology and evolution. Experiments leveraging evolution as a central feature put evolutionary theories to the test, and modern sequencing and engineering tools then characterized the molecular basis of adaptation. As theory and experimentations refined our understanding of evolution, a need to increase throughput and experimental complexity has emerged. Here, we summarize recent technologies that have made high-throughput experiments practical and highlight studies that have capitalized on these tools, defining an exciting new era in microbial experimental evolution. Multiple research directions previously limited by experimental scale are now accessible for study and we believe applying evolutionary lessons from in vitro studies onto these applied settings has the potential for major innovations and discoveries across ecology and medicine.

Microbiome breeding: conceptual and practical issues

Microbiome breeding is a new artificial selection technique that seeks to change the genetic composition of microbiomes in order to benefit plant or animal hosts. Recent experimental and theoretical analyses have shown that microbiome breeding is possible whenever microbiome-encoded genetic factors affect host traits (e.g., health) and microbiomes are transmissible between hosts with sufficient fidelity, such as during natural microbiome transmission between individuals of social animals, or during experimental microbiome transplanting between plants. To address misunderstandings that stymie microbiome-breeding programs, we (i) clarify and visualize the corresponding elements of microbiome selection and standard selection; (ii) elucidate the eco-evolutionary processes underlying microbiome selection within a quantitative genetic framework to summarize practical guidelines that optimize microbiome breeding; and (iii) characterize the kinds of host species most amenable to microbiome breeding.

Optimization of the 16S rRNA sequencing analysis pipeline for studying in vitro communities of gut commensals

While microbial communities inhabit a wide variety of complex natural environments, in vitro culturing enables highly controlled conditions and high-throughput interrogation for generating mechanistic insights. In vitro assemblies of gut commensals have recently been introduced as models for the intestinal microbiota, which plays fundamental roles in host health. However, a protocol for 16S rRNA sequencing and analysis of in vitro samples that optimizes financial cost, time/effort, and accuracy/reproducibility has yet to be established. Here, we systematically identify protocol elements that have significant impact, introduce bias, and/or can be simplified. Our results indicate that community diversity and composition are generally unaffected by substantial protocol streamlining. Additionally, we demonstrate that a strictly aerobic halophile is an effective spike-in for estimating absolute abundances in communities of anaerobic gut commensals. This time- and money-saving protocol should accelerate discovery by increasing 16S rRNA data reliability and comparability and through the incorporation of absolute abundance estimates.

Evolutionary dynamics, evolutionary forces, and robustness: A nonequilibrium statistical mechanics perspective

Evolution through natural selection is an overwhelmingly complex process, and it is not surprising that theoretical approaches are strongly simplifying it. For instance, population genetics considers mainly dynamics of gene allele frequencies. Here, we develop a complementary approach to evolutionary dynamics based on three elements—organism reproduction, variations, and selection—that are essential for any evolutionary theory. By considering such general dynamics as a stochastic thermodynamic process, we clarify the nature and action of the evolutionary forces. We show that some of the forces cannot be described solely in terms of fitness landscapes. We also find that one force contribution can make organism reproduction insensitive (robust) to variations.

Any realistic evolutionary theory has to consider 1) the dynamics of organisms that reproduce and possess heritable traits, 2) the appearance of stochastic variations in these traits, and 3) the selection of those organisms that better survive and reproduce. These elements shape the “evolutionary forces” that characterize the evolutionary dynamics. Here, we introduce a general model of reproduction–variation–selection dynamics. By treating these dynamics as a nonequilibrium thermodynamic process, we make precise the notion of the forces that characterize evolution. One of these forces, in particular, can be associated with the robustness of reproduction to variations. Some of the detailed predictions of our model can be tested by quantitative laboratory experiments, similar to those performed in the past on evolving populations of proteins or viruses.

Functional attractors in microbial community assembly

For microbiome biology to become a more predictive science, we must identify which descriptive features of microbial communities are reproducible and predictable, which are not, and why. We address this question by experimentally studying parallelism and convergence in microbial community assembly in replicate glucose-limited habitats. Here, we show that the previously observed family-level convergence in these habitats reflects a reproducible metabolic organization, where the ratio of the dominant metabolic groups can be explained from a simple resource-partitioning model. In turn, taxonomic divergence among replicate communities arises from multistability in population dynamics. Multistability can also lead to alternative functional states in closed ecosystems but not in metacommunities. Our findings empirically illustrate how the evolutionary conservation of quantitative metabolic traits, multistability, and the inherent stochasticity of population dynamics, may all conspire to generate the patterns of reproducibility and variability at different levels of organization that are commonplace in microbial community assembly.

Artificial Selection on Microbiomes To Breed Microbiomes That Confer Salt Tolerance to Plants

We develop a method to artificially select for rhizosphere microbiomes that confer salt tolerance to the model grass Brachypodium distachyon grown under sodium salt stress or aluminum salt stress. In a controlled greenhouse environment, we differentially propagated rhizosphere microbiomes between plants of a nonevolving, highly inbred plant population; therefore, only microbiomes evolved in our experiment, but the plants did not evolve in parallel. To maximize microbiome perpetuation when transplanting microbiomes between plants and, thus, maximize response to microbiome selection, we improved earlier methods by (i) controlling microbiome assembly when inoculating seeds at the beginning of each selection cycle; (ii) fractionating microbiomes before transfer between plants to harvest, perpetuate, and select on only bacterial and viral microbiome components; (iii) ramping of salt stress gradually from minor to extreme salt stress with each selection cycle to minimize the chance of overstressing plants; (iv) using two nonselection control treatments (e.g., nonselection microbial enrichment and null inoculation) that permit comparison to the improving fitness benefits that selected microbiomes impart on plants. Unlike previous methods, our selection protocol generated microbiomes that enhance plant fitness after only 1 to 3 rounds of microbiome selection. After nine rounds of microbiome selection, the effect of microbiomes selected to confer tolerance to aluminum salt stress was nonspecific (these artificially selected microbiomes equally ameliorate sodium and aluminum salt stresses), but the effect of microbiomes selected to confer tolerance to sodium salt stress was specific (these artificially selected microbiomes do not confer tolerance to aluminum salt stress). Plants with artificially selected microbiomes had 55 to 205% greater seed production than plants with unselected control microbiomes. IMPORTANCE We developed an experimental protocol that improves earlier methods of artificial selection on microbiomes and then tested the efficacy of our protocol to breed root-associated bacterial microbiomes that confer salt tolerance to a plant. Salt stress limits growth and seed production of crop plants, and artificially selected microbiomes conferring salt tolerance may ultimately help improve agricultural productivity. Unlike previous experiments of microbiome selection, our selection protocol generated microbiomes that enhance plant productivity after only 1 to 3 rounds of artificial selection on root-associated microbiomes, increasing seed production under extreme salt stress by 55 to 205% after nine rounds of microbiome selection. Although we artificially selected microbiomes under controlled greenhouse conditions that differ from outdoor conditions, increasing seed production by 55 to 205% under extreme salt stress is a remarkable enhancement of plant productivity compared to traditional plant breeding. We describe a series of additional experimental protocols that will advance insights into key parameters that determine efficacy and response to microbiome selection.

Synthetic Biology Toolkits and Metabolic Engineering Applied in Corynebacterium glutamicum for Biomanufacturing

Corynebacterium glutamicum is an important workhorse in industrial white biotechnology. It has been widely applied in the producing processes of amino acids, fuels, and diverse value-added chemicals. With the continuous disclosure of genetic regulation mechanisms, various strategies and technologies of synthetic biology were used to design and construct C. glutamicum cells for biomanufacturing and bioremediation. This study mainly aimed to summarize the design and construction strategies of C. glutamicum-engineered strains, which were based on genomic modification, synthetic biological device-assisted metabolic flux optimization, and directed evolution-based engineering. Then, taking two important bioproducts (N-acetylglucosamine and hyaluronic acid) as examples, the applications of C. glutamicum cell factories were introduced. Finally, we discussed the current challenges and future development trends of C. glutamicum-engineered strain construction.

Predictive microbial-based modelling of wheat yields and grain baking quality across a 500km transect in Québec

Crops yield and quality are difficult to predict using soil physico-chemical parameters. Because of their key roles in nutrient cycles, we hypothesized that there is an untapped predictive potential in the soil microbial communities. To test our hypothesis, we sampled soils across 80 wheat fields of the province of Quebec at the beginning of the growing season in May-June. We used a wide array of methods to characterize the microbial communities, their functions, and activities, including: 1) amplicon sequencing, 2) real-time PCR quantification, and 3) community-level substrate utilization. We also measured grain yield and quality at the end of the growing season, and key soil parameters at sampling. The diversity of fungi, the abundance of nitrification genes, and the use of specific organic carbon sources were often the best predictors for wheat yield and grain quality. Using 11 or less parameters, we were able to explain 64 to 90% of the variation in wheat yield and grain and flour quality across the province of Quebec. Microbial-based regression models outperformed basic soil-based models for predicting wheat quality indicators. Our results suggest that the measurement of microbial parameters early in the season could help predict accurately grain quality and quantity.

Artificial selection of stable rhizosphere microbiota leads to heritable plant phenotype changes

Artificial selection of microbiota opens new avenues for improving plants. However, reported results lack consistency. We hypothesised that the success in artificial selection of microbiota depends on the stabilisation of community structure. In a ten-generation experiment involving 1,800 plants, we selected rhizosphere microbiota of Brachypodium distachyon associated with high or low leaf greenness, a proxy of plant performance. The microbiota structure showed strong fluctuations during an initial transitory phase, with no detectable leaf greenness heritability. After five generations, the microbiota structure stabilised, concomitantly with heritability in leaf greenness. Selection, initially ineffective, did successfully alter the selected property as intended, especially for high selection. We show a remarkable correlation between the variability in plant traits and selected microbiota structures, revealing two distinct sub-communities associated with high or low leaf greenness, whose abundance was significantly steered by directional selection. Understanding microbiota structure stabilisation will improve the reliability of artificial microbiota selection.

Properties of Equilibria and Glassy Phases of the Random Lotka-Volterra Model with Demographic Noise

We study a reference model in theoretical ecology, the disordered Lotka-Volterra model for ecological communities, in the presence of finite demographic noise. Our theoretical analysis, valid for symmetric interactions, shows that for sufficiently heterogeneous interactions and low demographic noise the system displays a multiple equilibria phase, which we fully characterize. In particular, we show that in this phase the number of locally stable equilibria is exponential in the number of species. Upon further decreasing the demographic noise, we unveil the presence of a second transition like the so-called "Gardner" transition to a marginally stable phase similar to that observed in the jamming of amorphous materials. We confirm and complement our analytical results by numerical simulations. Furthermore, we extend their relevance by showing that they hold for other interacting random dynamical systems such as the random replicant model. Finally, we discuss their extension to the case of asymmetric couplings.

Geometrically regulating evolutionary dynamics in biofilms

The theoretical understanding of evolutionary dynamics in spatially structured populations often relies on nonspatial models. Biofilms are among such populations where a more accurate understanding is of theoretical interest and can reveal new solutions to existing challenges. Here, we studied how the geometry of the environment affects the evolutionary dynamics of expanding populations, using the Eden model. Our results show that fluctuations of subpopulations during range expansion in two- and three-dimensional environments are not Brownian. Furthermore, we found that the substrate's geometry interferes with the evolutionary dynamics of populations that grow upon it. Inspired by these findings, we propose a periodically wedged pattern on surfaces prone to develop biofilms. On such patterned surfaces, natural selection becomes less effective and beneficial mutants would have a harder time establishing. Additionally, this modification accelerates genetic drift and leads to less diverse biofilms. Both interventions are highly desired for biofilms.

Functional biology in its natural context: A search for emergent simplicity

The immeasurable complexity at every level of biological organization creates a daunting task for understanding biological function. Here, we highlight the risks of stripping it away at the outset and discuss a possible path toward arriving at emergent simplicity of understanding while still embracing the ever-changing complexity of biotic interactions that we see in nature.

Multi-Replicated Enrichment Communities as a Model System in Microbial Ecology

Recent advances in robotics and affordable genomic sequencing technologies have made it possible to establish and quantitatively track the assembly of enrichment communities in high-throughput. By conducting community assembly experiments in up to thousands of synthetic habitats, where the extrinsic sources of variation among replicates can be controlled, we can now study the reproducibility and predictability of microbial community assembly at different levels of organization, and its relationship with nutrient composition and other ecological drivers. Through a dialog with mathematical models, high-throughput enrichment communities are bringing us closer to the goal of developing a quantitative predictive theory of microbial community assembly. In this short review, we present an overview of recent research on this growing field, highlighting the connection between theory and experiments and suggesting directions for future work.

Enhancing Microbial Pollutant Degradation by Integrating Eco-Evolutionary Principles with Environmental Biotechnology

Environmental accumulation of anthropogenic pollutants is a pressing global issue. The biodegradation of these pollutants by microbes is an emerging field but is hampered by inefficient degradation rates and a limited knowledge of potential enzymes and pathways. Here, we advocate the view that significant progress can be achieved by harnessing artificial community selection for a desired biological process, an approach that makes use of eco-evolutionary principles. The selected communities can either be directly used in bioremediation applications or further be analyzed and modified, for instance through a combination of systems biology, synthetic biology, and genetic engineering. This knowledge can then inform machine learning and enhance the discovery of novel biodegradation pathways.

Directed Evolution of a Glutathione Transferase for the Development of a Biosensor for Alachlor Determination

In the present work, DNA recombination of three homologous tau class glutathione transferases (GSTUs) allowed the creation of a library of tau class GmGSTUs. The library was activity screened for the identification of glutathione transferase (GST) variants with enhanced catalytic activity towards the herbicide alachlor (2-chloro-2′,6′-diethyl-N-(methoxymethyl)acetanilide). One enzyme variant (GmGSTsf) with improved catalytic activity and binding affinity for alachlor was identified and explored for the development of an optical biosensor for alachlor determination. Kinetics analysis and molecular modeling studies revealed a key mutation (Ile69Val) at the subunit interface (helix α3) that appeared to be responsible for the altered catalytic properties. The enzyme was immobilized directly on polyvinylidenefluoride membrane by crosslinking with glutaraldehyde and was placed on the inner surface of a plastic cuvette. The rate of pH changes observed as a result of the enzyme reaction was followed optometrically using a pH indicator. A calibration curve indicated that the linear concentration range for alachlor was 30–300 μM. The approach used in the present study can provide tools for the generation of novel enzymes for eco-efficient and environment-friendly analytical technologies. In addition, the outcome of this study gives an example for harnessing protein symmetry for enzyme design.