Designing Metabolic Division of Labor in Microbial Communities

Cross-biome microbial networks reveal functional redundancy and suggest genome reduction through functional complementarity

The structure of microbial communities arises from a multitude of factors, including the interactions of microorganisms with each other and with the environment. In this work, we sought to disentangle those drivers by performing a cross-study, cross-biome meta-analysis of microbial occurrence data in more than 5000 samples, applying a novel network clustering algorithm aimed to capture conditional taxa co-occurrences. We then examined the phylogenetic and functional composition of the resulting clusters, and searched for global patterns of assembly both at the community level and in the presence/absence of individual metabolic pathways.Our analysis highlighted the prevalence of functional redundancy in microbial communities, particularly between taxa that co-occur in more than one environment, pointing to a relationship between functional redundancy and environmental adaptation. In spite of this, certain pathways were observed in fewer taxa than expected by chance, suggesting the presence of auxotrophy, and presumably cooperation among community members. This hypothetical cooperation may play a role in genome reduction, since we observed a negative relationship between the size of bacterial genomes and the size of the community they belong to.Overall, our results suggest the microbial community assembly is driven by universal principles that operate consistently across different biomes and taxonomic groups.

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.

Context-dependent redesign of robust synthetic gene circuits

Cells provide dynamic platforms for executing exogenous genetic programs in synthetic biology, resulting in highly context-dependent circuit performance. Recent years have seen an increasing interest in understanding the intricacies of circuit-host relationships, their influence on the synthetic bioengineering workflow, and in devising strategies to alleviate undesired effects. We provide an overview of how emerging circuit-host interactions, such as growth feedback and resource competition, impact both deterministic and stochastic circuit behaviors. We also emphasize control strategies for mitigating these unwanted effects. This review summarizes the latest advances and the current state of host-aware and resource-aware design of synthetic gene circuits.

Whole genome sequencing revealed the capability of Paenarthrobacter sp. KN0901 to simultaneously remove atrazine and corn straw at low temperatures: From gene identification to empirical validation

Corn planting is often associated with serious atrazine pollution and excessive corn straw amounts, causing severe threats to environmental and ecological security, as well as to green agricultural development. In this context, a Paenarthrobacter sp. KN0901 strain was applied to simultaneously remove atrazine and straw at low temperatures. The results of whole genome sequencing indicated that KN0901 encoded over nine straw biodegradation-related enzymes. In addition, 100 % and 27.3 % of atrazine and straw were simultaneously degraded by KN0901 following an incubation period of seven days at 15 ºC and 180 rpm in darkness. The KN0901 strain maintained high atrazine and straw biodegradation rates under temperature and pH ranges of 4-25 ºC and 5-9, respectively. The simultaneous atrazine and corn straw additions improved the microbial growth and biodegradation rates by increasing the functional gene expression level, cell viability, inner membrane permeability, and extracellular polymeric substance contents of KN0901. The hydroponic experiment results demonstrated the capability of the KN0901 strain to mitigate the toxicity of atrazine to soybeans in four days under the presence of corn straw. The present study provides a new perspective on the development of bioremediation approaches and their application to restore atrazine-polluted cornfields with large straw quantities, particularly in cold areas.

Rational construction of synthetic consortia: Key considerations and model-based methods for guiding the development of a novel biosynthesis platform

The rapid development of synthetic biology has significantly improved the capabilities of mono-culture systems in converting different substrates into various value-added bio-chemicals through metabolic engineering. However, overexpression of biosynthetic pathways in recombinant strains can impose a heavy metabolic burden on the host, resulting in imbalanced energy distribution and negatively affecting both cell growth and biosynthesis capacity. Synthetic consortia, consisting of two or more microbial species or strains with complementary functions, have emerged as a promising and efficient platform to alleviate the metabolic burden and increase product yield. However, research on synthetic consortia is still in its infancy, with numerous challenges regarding the design and construction of stable synthetic consortia. This review provides a comprehensive comparison of the advantages and disadvantages of mono-culture systems and synthetic consortia. Key considerations for engineering synthetic consortia based on recent advances are summarized, and simulation and computational tools for guiding the advancement of synthetic consortia are discussed. Moreover, further development of more efficient and cost-effective synthetic consortia with emerging technologies such as artificial intelligence and machine learning is highlighted.

Programming Dynamic Division of Labor Using Horizontal Gene Transfer

The metabolic engineering of microbes has broad applications, including biomanufacturing, bioprocessing, and environmental remediation. The introduction of a complex, multistep pathway often imposes a substantial metabolic burden on the host cell, restraining the accumulation of productive biomass and limiting pathway efficiency. One strategy to alleviate metabolic burden is the division of labor (DOL) in which different subpopulations carry out different parts of the pathway and work together to convert a substrate into a final product. However, the maintenance of different engineered subpopulations is challenging due to competition and convoluted interstrain population dynamics. Through modeling, we show that dynamic division of labor (DDOL), which we define as the DOL between indiscrete populations capable of dynamic and reversible interchange, can overcome these limitations and enable the robust maintenance of burdensome, multistep pathways. We propose that DDOL can be mediated by horizontal gene transfer (HGT) and use plasmid genomics to uncover evidence that DDOL is a strategy utilized by natural microbial communities. Our work suggests that bioengineers can harness HGT to stabilize synthetic metabolic pathways in microbial communities, enabling the development of robust engineered systems for deployment in a variety of contexts.

Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations

The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology.

Flux sampling in genome-scale metabolic modeling of microbial communities

BACKGROUND

Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions and identify strategies to optimize the community. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model; however, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling can capture additional heterogeneity across a population, especially when cells exhibit sub-maximal growth rates.

RESULTS

In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. With sampling, we find significant differences in the predicted metabolism, including an increase in cooperative interactions and pathway-specific changes in predicted flux.

CONCLUSIONS

Our results suggest the importance of sampling-based approaches to evaluate metabolic interactions. Furthermore, we emphasize the utility of flux sampling in quantitatively studying interactions between cells and organisms.

The trade-off between individual metabolic specialization and versatility determines the metabolic efficiency of microbial communities

In microbial systems, a metabolic pathway can be either completed by one autonomous population or distributed among a consortium performing metabolic division of labor (MDOL). MDOL facilitates the system's function by reducing the metabolic burden; however, it may hinder the function by reducing the exchange efficiency of metabolic intermediates among individuals. As a result, the function of a community is influenced by the trade-offs between the metabolic specialization and versatility of individuals. To experimentally test this hypothesis, we deconstructed the naphthalene degradation pathway into four steps and introduced them individually or combinatorically into different strains with varying levels of metabolic specialization. Using these strains, we engineered 1,456 synthetic consortia and found that 74 consortia exhibited higher degradation function than both the autonomous population and rigorous MDOL consortium. Quantitative modeling provides general strategies for identifying the most effective MDOL configuration. Our study provides critical insights into the engineering of high-performance microbial systems.

Searching for Principles of Microbial Ecology Across Levels of Biological Organization

Microbial communities play pivotal roles in ecosystems across different scales, from global elemental cycles to household food fermentations. These complex assemblies comprise hundreds or thousands of microbial species whose abundances vary over time and space. Unraveling the principles that guide their dynamics at different levels of biological organization, from individual species, their interactions, to complex microbial communities, is a major challenge. To what extent are these different levels of organization governed by separate principles, and how can we connect these levels to develop predictive models for the dynamics and function of microbial communities? Here, we will discuss recent advances that point towards principles of microbial communities, rooted in various disciplines from physics, biochemistry, and dynamical systems. By considering the marine carbon cycle as a concrete example, we demonstrate how the integration of levels of biological organization can offer deeper insights into the impact of increasing temperatures, such as those associated with climate change, on ecosystem-scale processes. We argue that by focusing on principles that transcend specific microbiomes, we can pave the way for a comprehensive understanding of microbial community dynamics and the development of predictive models for diverse ecosystems.

Metabolic cross-feeding enhances branched-chain aldehydes production in a synthetic community of fermented sausages

Microbial interactions play an important role in regulating the metabolic function of fermented food communities, especially the production of key flavor compounds. However, little is known about specific molecular mechanisms that regulate the production of key flavor compounds through microbial interactions. Here, we designed a synthetic consortium containing Debaryomyces hansenii D1, Staphylococcus xylosus S1, and Pediococcus pentosaceus PP1 to explore the mechanism of the microbial interactions underlying the branched-chain aldehydes production. In this consortium, firstly, D. hansenii secreted amino acids that promoted the growth of P. pentosaceus and S. xylosus. Specifically, D. hansenii D1 secreted alanine, aspartate, glutamate, glutamine, glycine, phenylalanine, serine, and threonine, which were the primary nutrients for bacterial growth. P. pentosaceus PP1 utilized all these eight amino acids through cross-feeding, whereas S. xylosus S1 did not utilize aspartate and serine. Furthermore, D. hansenii D1 promoted the production of branched-chain aldehydes from S. xylosus and P. pentosaceus through cross-feeding of α-keto acids (intermediate metabolites). Thus, the accumulation of 2-methyl-butanal was promoted in all co-culture. Overall, this work revealed the mechanism by which D. hansenii and bacteria cross-feed to produce branched-chain aldehydes in fermented sausages.

Coexistence of specialist and generalist species within mixed plastic derivative-utilizing microbial communities

BACKGROUND

Plastic-degrading microbial isolates offer great potential to degrade, transform, and upcycle plastic waste. Tandem chemical and biological processing of plastic wastes has been shown to substantially increase the rates of plastic degradation; however, the focus of this work has been almost entirely on microbial isolates (either bioengineered or naturally occurring). We propose that a microbial community has even greater potential for plastic upcycling. A microbial community has greater metabolic diversity to process mixed plastic waste streams and has built-in functional redundancy for optimal resilience.

RESULTS

Here, we used two plastic-derivative degrading communities as a model system to investigate the roles of specialist and generalist species within the microbial communities. These communities were grown on five plastic-derived substrates: pyrolysis treated high-density polyethylene, chemically deconstructed polyethylene terephthalate, disodium terephthalate, terephthalamide, and ethylene glycol. Short-read metagenomic and metatranscriptomic sequencing were performed to evaluate activity of microorganisms in each treatment. Long-read metagenomic sequencing was performed to obtain high-quality metagenome assembled genomes and evaluate division of labor.

CONCLUSIONS

Data presented here show that the communities are primarily dominated by Rhodococcus generalists and lower abundance specialists for each of the plastic-derived substrates investigated here, supporting previous research that generalist species dominate batch culture. Additionally, division of labor may be present between Hydrogenophaga terephthalate degrading specialists and lower abundance protocatechuate degrading specialists. Video Abstract.

A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia

Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.

Metagenome-based metabolic modelling predicts unique microbial interactions in deep-sea hydrothermal plume microbiomes

Deep-sea hydrothermal vents are abundant on the ocean floor and play important roles in ocean biogeochemistry. In vent ecosystems such as hydrothermal plumes, microorganisms rely on reduced chemicals and gases in hydrothermal fluids to fuel primary production and form diverse and complex microbial communities. However, microbial interactions that drive these complex microbiomes remain poorly understood. Here, we use microbiomes from the Guaymas Basin hydrothermal system in the Pacific Ocean to shed more light on the key species in these communities and their interactions. We built metabolic models from metagenomically assembled genomes (MAGs) and infer possible metabolic exchanges and horizontal gene transfer (HGT) events within the community. We highlight possible archaea-archaea and archaea-bacteria interactions and their contributions to the robustness of the community. Cellobiose, D-Mannose 1-phosphate, O2, CO2, and H2S were among the most exchanged metabolites. These interactions enhanced the metabolic capabilities of the community by exchange of metabolites that cannot be produced by any other community member. Archaea from the DPANN group stood out as key microbes, benefiting significantly as acceptors in the community. Overall, our study provides key insights into the microbial interactions that drive community structure and organisation in complex hydrothermal plume microbiomes.

Flux Sampling in Genome-scale Metabolic Modeling of Microbial Communities

Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model. However, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling may capture additional heterogeneity across cells, especially when cells exhibit sub-maximal growth rates. In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. We find significant differences in the predicted metabolism with sampling, including increased cooperative interactions and pathway-specific changes in predicted flux. Our results suggest the importance of sampling-based and objective function-independent approaches to evaluate metabolic interactions and emphasize their utility in quantitatively studying interactions between cells and organisms.

A Novel Microbial Consortia Catalysis Strategy for the Production of Hydroxytyrosol from Tyrosine

Hydroxytyrosol, a valuable plant-derived phenolic compound, is increasingly produced from microbial fermentation. However, the promiscuity of the key enzyme HpaBC, the two-component flavin-dependent monooxygenase from Escherichia coli, often leads to low yields. To address this limitation, we developed a novel strategy utilizing microbial consortia catalysis for hydroxytyrosol production. We designed a biosynthetic pathway using tyrosine as the substrate and selected enzymes and overexpressing glutamate dehydrogenase GdhA to realize the cofactor cycling by coupling reactions catalyzed by the transaminase and the reductase. Additionally, the biosynthetic pathway was divided into two parts and performed by separate E. coli strains. Furthermore, we optimized the inoculation time, strain ratio, and pH to maximize the hydroxytyrosol yield. Glycerol and ascorbic acid were added to the co-culture, resulting in a 92% increase in hydroxytyrosol yield. Using this approach, the production of 9.2 mM hydroxytyrosol was achieved from 10 mM tyrosine. This study presents a practical approach for the microbial production of hydroxytyrosol that can be promoted to produce other value-added compounds.

Genome-scale community modeling for deciphering the inter-microbial metabolic interactions in fungus-farming termite gut microbiome

Specialized microbial communities in the fungus-farming termite gut and fungal comb microbiome help maintain host nutrition through interactive biochemical activities of complex carbohydrate degradation. Numerous research studies have been focused on identifying the microbial species in the termite gut and fungal comb microbiota, but the community-wide metabolic interaction patterns remain obscure. The inter-microbial metabolic interactions in the community environment are essential for executing biochemical processes like complex carbohydrate degradation and maintaining the host's physicochemical homeostasis. Recent progress in high-throughput sequencing techniques and mathematical modeling provides suitable platforms for constructing multispecies genome-scale community metabolic models that can render sound knowledge about microbial metabolic interaction patterns. Here, we have implemented the genome-scale metabolic modeling strategy to map the relationship between genes, proteins, and reactions of 12 key bacterial species from fungal cultivating termite gut and fungal comb microbiota. The resulting individual genome-scale metabolic models (GEMs) have been analyzed using flux balance analysis (FBA) to optimize the metabolic flux distribution pattern. Further, these individual GEMs have been integrated into genome-scale community metabolic models where a heuristics-based computational procedure has been employed to track the inter-microbial metabolic interactions. Two separate genome-scale community metabolic models were reconstructed for the O. badius gut and fungal comb microbiome. Analysis of the community models showed up to ∼167% increased flux range in lignocellulose degradation, amino acid biosynthesis, and nucleotide metabolism pathways. The inter-microbial metabolic exchange of amino acids, SCFAs, and small sugars was also upregulated in the multispecies community for maximum biomass formation. The flux variability analysis (FVA) has also been performed to calculate the feasible flux range of metabolic reactions. Furthermore, based on the calculated metabolic flux values, newly defined parameters, i.e., pairwise metabolic assistance (PMA) and community metabolic assistance (CMA) showed that the microbial species are getting up to 15% higher metabolic benefits in the multispecies community compared to pairwise growth. Assessment of the inter-microbial metabolic interaction patterns through pairwise growth support index (PGSI) indicated an increased mutualistic interaction in the termite gut environment compared to the fungal comb. Thus, this genome-scale community modeling study provides a systematic methodology to understand the inter-microbial interaction patterns with several newly defined parameters like PMA, CMA, and PGSI. The microbial metabolic assistance and interaction patterns derived from this computational approach will enhance the understanding of combinatorial microbial activities and may help develop effective synergistic microcosms to utilize complex plant polymers.

Gut-on-a-Chip for the Analysis of Bacteria-Bacteria Interactions in Gut Microbial Community: What Would Be Needed for Bacterial Co-Culture Study to Explore the Diet-Microbiota Relationship?

Bacterial co-culture studies using synthetic gut microbiomes have reported novel research designs to understand the underlying role of bacterial interaction in the metabolism of dietary resources and community assembly of complex microflora. Since lab-on-a-chip mimicking the gut (hereafter "gut-on-a-chip") is one of the most advanced platforms for the simulative research regarding the correlation between host health and microbiota, the co-culture of the synthetic bacterial community in gut-on-a-chip is expected to reveal the diet-microbiota relationship. This critical review analyzed recent research on bacterial co-culture with perspectives on the ecological niche of commensals, probiotics, and pathogens to categorize the experimental approaches for diet-mediated management of gut health as the compositional and/or metabolic modulation of the microbiota and the control of pathogens. Meanwhile, the aim of previous research on bacterial culture in gut-on-a-chip has been mainly limited to the maintenance of the viability of host cells. Thus, the integration of study designs established for the co-culture of synthetic gut consortia with various nutritional resources into gut-on-a-chip is expected to reveal bacterial interspecies interactions related to specific dietary patterns. This critical review suggests novel research topics for co-culturing bacterial communities in gut-on-a-chip to realize an ideal experimental platform mimicking a complex intestinal environment.

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 framework for the targeted recruitment of crop-beneficial soil taxa based on network analysis of metagenomics data

BACKGROUND

The design of ecologically sustainable and plant-beneficial soil systems is a key goal in actively manipulating root-associated microbiomes. Community engineering efforts commonly seek to harness the potential of the indigenous microbiome through substrate-mediated recruitment of beneficial members. In most sustainable practices, microbial recruitment mechanisms rely on the application of complex organic mixtures where the resources/metabolites that act as direct stimulants of beneficial groups are not characterized. Outcomes of such indirect amendments are unpredictable regarding engineering the microbiome and achieving a plant-beneficial environment.

RESULTS

This study applied network analysis of metagenomics data to explore amendment-derived transformations in the soil microbiome, which lead to the suppression of pathogens affecting apple root systems. Shotgun metagenomic analysis was conducted with data from 'sick' vs 'healthy/recovered' rhizosphere soil microbiomes. The data was then converted into community-level metabolic networks. Simulations examined the functional contribution of treatment-associated taxonomic groups and linked them with specific amendment-induced metabolites. This analysis enabled the selection of specific metabolites that were predicted to amplify or diminish the abundance of targeted microbes functional in the healthy soil system. Many of these predictions were corroborated by experimental evidence from the literature. The potential of two of these metabolites (dopamine and vitamin B₁₂) to either stimulate or suppress targeted microbial groups was evaluated in a follow-up set of soil microcosm experiments. The results corroborated the stimulant's potential (but not the suppressor) to act as a modulator of plant beneficial bacteria, paving the way for future development of knowledge-based (rather than trial and error) metabolic-defined amendments. Our pipeline for generating predictions for the selective targeting of microbial groups based on processing assembled and annotated metagenomics data is available at https://github.com/ot483/NetCom2 .

CONCLUSIONS

This research demonstrates how genomic-based algorithms can be used to formulate testable hypotheses for strategically engineering the rhizosphere microbiome by identifying specific compounds, which may act as selective modulators of microbial communities. Applying this framework to reduce unpredictable elements in amendment-based solutions promotes the development of ecologically-sound methods for re-establishing a functional microbiome in agro and other ecosystems. Video Abstract.

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.

In silico prediction of potential indigenous microbial biomarkers in Penaeus vannamei identified through meta-analysis and genome-scale metabolic modelling

BACKGROUND

Understanding the microbiome is crucial as it contributes to the metabolic health of the host and, upon dysbiosis, may influence disease development. With the recent surge in high-throughput sequencing technology, the availability of microbial genomic data has increased dramatically. Amplicon sequence-based analyses majorly profile microbial abundance and determine taxonomic markers. Furthermore, the availability of genome sequences for various microbial organisms has prompted the integration of genome-scale metabolic modelling that provides insights into the metabolic interactions influencing host health. However, the analysis from a single study may not be consistent, necessitating a meta-analysis.

RESULTS

We conducted a meta-analysis and integrated with constraint-based metabolic modelling approach, focusing on the microbiome of pacific white shrimp Penaeus vannamei, an extensively cultured marine candidate species. Meta-analysis revealed that Acinetobacter and Alteromonas are significant indicators of "health" and "disease" specific taxonomic biomarkers, respectively. Further, we enumerated metabolic interactions among the taxonomic biomarkers by applying a constraint-based approach to the community metabolic models (4416 pairs). Under different nutrient environments, a constraint-based flux simulation identified five beneficial species: Acinetobacter spWCHA55, Acinetobacter tandoii SE63, Bifidobacterium pseudolongum 49 D6, Brevundimonas pondensis LVF1, and Lutibacter profundi LP1 mediating parasitic interactions majorly under sucrose environment in the pairwise community. The study also reports the healthy biomarkers that can co-exist and have functionally dependent relationships to maintain a healthy state in the host.

CONCLUSIONS

Toward this, we collected and re-analysed the amplicon sequence data of P. vannamei (encompassing 117 healthy and 142 disease datasets). By capturing the taxonomic biomarkers and modelling the metabolic interaction between them, our study provides a valuable resource, a first-of-its-kind analysis in aquaculture scenario toward a sustainable shrimp farming.

Multi-genome metabolic modeling predicts functional inter-dependencies in the Arabidopsis root microbiome

BACKGROUND

From a theoretical ecology point of view, microbiomes are far more complex than expected. Besides competition and competitive exclusion, cooperative microbe-microbe interactions have to be carefully considered. Metabolic dependencies among microbes likely explain co-existence in microbiota.

METHODOLOGY

In this in silico study, we explored genome-scale metabolic models (GEMs) of 193 bacteria isolated from Arabidopsis thaliana roots. We analyzed their predicted producible metabolites under simulated nutritional constraints including "root exudate-mimicking growth media" and assessed the potential of putative metabolic exchanges of by- and end-products to avoid those constraints.

RESULTS

We found that the genome-encoded metabolic potential is quantitatively and qualitatively clustered by phylogeny, highlighting metabolic differentiation between taxonomic groups. Random, synthetic combinations of increasing numbers of strains (SynComs) indicated that the number of producible compounds by GEMs increased with average phylogenetic distance, but that most SynComs were centered around an optimal phylogenetic distance. Moreover, relatively small SynComs could reflect the capacity of the whole community due to metabolic redundancy. Inspection of 30 specific end-product metabolites (i.e., target metabolites: amino acids, vitamins, phytohormones) indicated that the majority of the strains had the genetic potential to produce almost all the targeted compounds. Their production was predicted (1) to depend on external nutritional constraints and (2) to be facilitated by nutritional constraints mimicking root exudates, suggesting nutrient availability and root exudates play a key role in determining the number of producible metabolites. An answer set programming solver enabled the identification of numerous combinations of strains predicted to depend on each other to produce these targeted compounds under severe nutritional constraints thus indicating a putative sub-community level of functional redundancy.

CONCLUSIONS

This study predicts metabolic restrictions caused by available nutrients in the environment. By extension, it highlights the importance of the environment for niche potential, realization, partitioning, and overlap. Our results also suggest that metabolic dependencies and cooperation among root microbiota members compensate for environmental constraints and help maintain co-existence in complex microbial communities. Video Abstract.

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.

Predictive evolution of metabolic phenotypes using model-designed environments

Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution.

Bacterial species metabolic interaction network for deciphering the lignocellulolytic system in fungal cultivating termite gut microbiota

Fungus-cultivating termite Odontotermes badius developed a mutualistic association with Termitomyces fungi for the plant material decomposition and providing a food source for the host survival. The mutualistic relationship sifted the microbiome composition of the termite gut and Termitomyces fungal comb. Symbiotic bacterial communities in the O. badius gut and fungal comb have been studied extensively to identify abundant bacteria and their lignocellulose degradation capabilities. Despite several metagenomic studies, the species-wide metabolic interaction pattern of bacterial communities in termite gut and fungal comb remains unclear. The bacterial species metabolic interaction network (BSMIN) has been constructed with 230 bacteria identified from the O. badius gut and fungal comb microbiota. The network portrayed the metabolic map of the entire microbiota and highlighted several inter-species biochemical interactions like cross-feeding, metabolic interdependency, and competition. Further, the reconstruction and analysis of the bacterial influence network (BIN) quantified the positive and negative pairwise influences in the termite gut and fungal comb microbial communities. Several key macromolecule degraders and fermentative microbial entities have been identified by analyzing the BIN. The mechanistic interplay between these influential microbial groups and the crucial glycoside hydrolases (GH) enzymes produced by the macromolecule degraders execute the community-wide functionality of lignocellulose degradation and subsequent fermentation. The metabolic interaction pattern between the nine influential microbial species has been determined by considering them growing in a synthetic microbial community. Competition (30%), parasitism (47%), and mutualism (17%) were predicted to be the major mode of metabolic interaction in this synthetic microbial community. Further, the antagonistic metabolic effect was found to be very high in the metabolic-deprived condition, which may disrupt the community functionality. Thus, metabolic interactions of the crucial bacterial species and their GH enzyme cocktail identified from the O. badius gut and fungal comb microbiota may provide essential knowledge for developing a synthetic microcosm with efficient lignocellulolytic machinery.

A newly-constructed bifunctional bacterial consortium for removing butyl xanthate and cadmium simultaneously from mineral processing wastewater: Experimental evaluation, degradation and biomineralization

Due to the technological limitations associated with beneficiation technology, large amounts of flotation reagents and heavy metals remain in mineral processing wastewater. Unfortunately, however, no treatment methods are available to mitigate the resulting pollution by them. In this study, a bacterial consortium SDMC (simultaneously degrade butyl xanthate and biomineralize cadmium) was constructed in an effort to simultaneously degrade butyl xanthate (BX) and biomineralize cadmium (Cd) by screening and domesticating two different bacterial species including Hypomicrobium and Sporosarcina. SDMC is efficient in removing the combined pollution due to BX and Cd with a 100% degradation rate for BX and 99% biomineralization rate for Cd within 4 h. Besides, SDMC can tolerate high concentrations of Fe(III) (0-40 mg/L). It has an excellent ability to utilize Fe(III) for enhanced removal of the combined pollutants. SDMC can effectively remove pollutants with a pH range of 6-9. Further, we discussed pathways for potential degradation and biomineralization: Cd(BX)₂-Cd²⁺, BX^-; BX^--CS₂, butyl perxanthate (BPX); Cd²⁺-(Ca0.67,Cd0.33)CO₃. The removal of the combined pollutants primarily entails decomposition, degradation, and biomineralization, C-O bond cleavage, and microbially induced carbonate precipitation (MICP). SDMC is a simple, efficient, and eco-friendly bifunctional bacterial consortium for effective treatment of BX-Cd combined pollution in mineral processing wastewater.

Continuous Fermentation by Lactobacillus bulgaricus T15 Cells Immobilized in Cross-Linked F127 Hydrogels to Produce ᴅ-Lactic Acid

Lignocellulose biorefinery via continuous cell-recycle fermentation has long been recognized as a promising alternative technique for producing chemicals. ᴅ-lactic acid (D-LA) production by fermentation of corn stover by Lactobacillus bulgaricus was proven to be feasible by a previous study. However, the phenolic compounds and the high glucose content in this substrate may inhibit cell growth. The immobilization of cells in polymer hydrogels can protect them from toxic compounds in the medium and improve fermentation efficiency. Here, we studied the production of D-LA by L. bulgaricus cells immobilized in cross-linkable F127 bis-polyurethane methacrylate (F127-BUM/T15). The Hencky stress and Hencky strain of F127-BUM/T15 was 159.11 KPa and 0.646 respectively. When immobilized and free-living cells were cultured in media containing 5-hydroxymethylfurfural, vanillin, or high glucose concentrations, the immobilized cells were more tolerant, produced higher D-LA yields, and had higher sugar-to-acid conversion ratios. After 100 days of fermentation, the total D-LA production via immobilized cells was 1982.97 ± 1.81 g with a yield of 2.68 ± 0.48 g/L h, which was higher than that of free cells (0.625 ± 0.28 g/L h). This study demonstrated that F127-BUM/T15 has excellent potential for application in the biorefinery industry.

Metabolic modeling of the International Space Station microbiome reveals key microbial interactions

BACKGROUND

Recent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS and how they shape the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization.

RESULTS

Through a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modeling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence of K. pneumoniae is beneficial to many other microorganisms it coexists with, notably those from the Pantoea genus. Species belonging to the Enterobacteriaceae family were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However, K. pneumoniae was found to exhibit parasitic and amensalistic interactions with Aspergillus and Penicillium species, respectively. To prove this metabolic prediction, K. pneumoniae and Aspergillus fumigatus were co-cultured under normal and simulated microgravity, where K. pneumoniae cells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence of K. pneumoniae compromised the morphology of fungal conidia and degenerated its biofilm-forming structures.

CONCLUSION

Our study underscores the importance of K. pneumoniae in the ISS, and its potential positive and negative interactions with other microbes, including potential pathogens. This integrated modeling approach, combined with experiments, demonstrates the potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies. Video Abstract.

Metabolic modeling of the International Space Station microbiome reveals key microbial interactions

BACKGROUND

Recent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS and how they shape the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization.

RESULTS

Through a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modeling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence of K. pneumoniae is beneficial to many other microorganisms it coexists with, notably those from the Pantoea genus. Species belonging to the Enterobacteriaceae family were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However, K. pneumoniae was found to exhibit parasitic and amensalistic interactions with Aspergillus and Penicillium species, respectively. To prove this metabolic prediction, K. pneumoniae and Aspergillus fumigatus were co-cultured under normal and simulated microgravity, where K. pneumoniae cells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence of K. pneumoniae compromised the morphology of fungal conidia and degenerated its biofilm-forming structures.

CONCLUSION

Our study underscores the importance of K. pneumoniae in the ISS, and its potential positive and negative interactions with other microbes, including potential pathogens. This integrated modeling approach, combined with experiments, demonstrates the potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies. Video Abstract.

Toward merging bottom-up and top-down model-based designing of synthetic microbial communities

The increasing interest of microbial communities as promising biocatalyst is leading an intense effort into the development of computational frameworks assisting the analysis and rational engineering of such complex ecosystems. Here, we critically review the recent computational and model-guided advances in the system-level engineering of microbiome, including both the rational bottom-up and the evolutionary top-down approaches. Furthermore, we highlight modeling and computational methods supporting both engineering paradigms. Finally, we discuss the advantages of combining both strategies into a hybrid top-down/bottom-up (middle-out) strategy to engineer synthetic microbial communities with improved performance and scope.

Resource-aware whole-cell model of division of labour in a microbial consortium for complex-substrate degradation

BACKGROUND

Low-cost sustainable feedstocks are essential for commercially viable biotechnologies. These feedstocks, often derived from plant or food waste, contain a multitude of different complex biomolecules which require multiple enzymes to hydrolyse and metabolise. Current standard biotechnology uses monocultures in which a single host expresses all the proteins required for the consolidated bioprocess. However, these hosts have limited capacity for expressing proteins before growth is impacted. This limitation may be overcome by utilising division of labour (DOL) in a consortium, where each member expresses a single protein of a longer degradation pathway.

RESULTS

Here, we model a two-strain consortium, with one strain expressing an endohydrolase and a second strain expressing an exohydrolase, for cooperative degradation of a complex substrate. Our results suggest that there is a balance between increasing expression to enhance degradation versus the burden that higher expression causes. Once a threshold of burden is reached, the consortium will consistently perform better than an equivalent single-cell monoculture.

CONCLUSIONS

We demonstrate that resource-aware whole-cell models can be used to predict the benefits and limitations of using consortia systems to overcome burden. Our model predicts the region of expression where DOL would be beneficial for growth on starch, which will assist in making informed design choices for this, and other, complex-substrate degradation pathways.

Substrate availability and toxicity shape the structure of microbial communities engaged in metabolic division of labor

Metabolic division of labor (MDOL) represents a widespread natural phenomenon, whereby a complex metabolic pathway is shared between different strains within a community in a mutually beneficial manner. However, little is known about how the composition of such a microbial community is regulated. We hypothesized that when degradation of an organic compound is carried out via MDOL, the concentration and toxicity of the substrate modulate the benefit allocation between the two microbial populations, thus affecting the structure of this community. We tested this hypothesis by combining modeling with experiments using a synthetic consortium. Our modeling analysis suggests that the proportion of the population executing the first metabolic step can be simply estimated by Monod-like formulas governed by substrate concentration and toxicity. Our model and the proposed formula were able to quantitatively predict the structure of our synthetic consortium. Further analysis demonstrates that our rule is also applicable in estimating community structures in spatially structured environments. Together, our work clearly demonstrates that the structure of MDOL communities can be quantitatively predicted using available information on environmental factors, thus providing novel insights into how to manage artificial microbial systems for the wide application of the bioindustry.

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.

Cooperation increases robustness to ecological disturbance in microbial cross-feeding networks

Microorganisms mainly exist within complex networks of ecological interactions. Given that the growth and survival of community members frequently depend on an obligate exchange of essential metabolites, it is generally unclear how such communities can persist despite the destabilising force of ecological disturbance. Here we address this issue using a population dynamics model. In contrast to previous work that suggests the potential for obligate interaction networks to emerge is limited, we find the opposite pattern: ecological disturbance favours both specific network topologies and cooperative cross-feeding among community members. These results establish environmental perturbations as a key driver shaping the architecture of microbial interaction networks.

Obesity influences composition of salivary and fecal microbiota and impacts the interactions between bacterial taxa

Obesity is an increasing global health concern and is associated with a broad range of morbidities. The gut microbiota are increasingly recognized as important contributors to obesity and cardiometabolic health. This study aimed to characterize oral and gut microbial communities, and evaluate host: microbiota interactions between clinical obesity classifications. We performed 16S rRNA sequencing on fecal and salivary samples, global metabolomics profiling on plasma and stool samples, and dietary profiling in 135 healthy individuals. We grouped individuals by obesity status, based on body mass index (BMI), including lean (BMI 18-124.9), overweight (BMI 25-29.9), or obese (BMI ≥30). We analyzed differences in microbiome composition, community inter-relationships, and predicted microbial function by obesity status. We found that salivary bacterial communities of lean and obese individuals were compositionally and phylogenetically distinct. An increase in obesity status was positively associated with strong correlations between bacterial taxa, particularly with bacterial groups implicated in metabolic disorders including Fretibacterium, and Tannerella. Consumption of sweeteners, especially xylitol, significantly influenced compositional and phylogenetic diversities of salivary and fecal bacterial communities. In addition, obesity groups exhibited differences in predicted bacterial metabolic activity, which was correlated with host's metabolite concentrations. Overall, obesity was associated with distinct changes in bacterial community dynamics, particularly in saliva. Consideration of microbiome community structure and inclusion of salivary samples may improve our ability to understand pathways linking microbiota to obesity and cardiometabolic disease.

Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance

Microbial communities are composed of cells of varying metabolic capacity, and regularly include auxotrophs that lack essential metabolic pathways. Through analysis of auxotrophs for amino acid biosynthesis pathways in microbiome data derived from >12,000 natural microbial communities obtained as part of the Earth Microbiome Project (EMP), and study of auxotrophic–prototrophic interactions in self-establishing metabolically cooperating yeast communities (SeMeCos), we reveal a metabolically imprinted mechanism that links the presence of auxotrophs to an increase in metabolic interactions and gains in antimicrobial drug tolerance. As a consequence of the metabolic adaptations necessary to uptake specific metabolites, auxotrophs obtain altered metabolic flux distributions, export more metabolites and, in this way, enrich community environments in metabolites. Moreover, increased efflux activities reduce intracellular drug concentrations, allowing cells to grow in the presence of drug levels above minimal inhibitory concentrations. For example, we show that the antifungal action of azoles is greatly diminished in yeast cells that uptake metabolites from a metabolically enriched environment. Our results hence provide a mechanism that explains why cells are more robust to drug exposure when they interact metabolically.

Using microbiome data analysis and a self-establishing metabolically cooperating yeast community model, the authors show that the presence of auxotrophs in a microbial community increases metabolic interactions between cells and fosters antimicrobial drug tolerance.

Programmable living assembly of materials by bacterial adhesion

The field of engineered living materials aims to construct functional materials with desirable properties of natural living systems. A recent study demonstrated the programmed self-assembly of bacterial populations by engineered adhesion. Here we use this strategy to engineer self-healing living materials with versatile functions. Bacteria displaying outer membrane-anchored nanobody-antigen pairs are cultured separately and, when mixed, adhere to each other to enable processing into functional materials, which we term living assembled material by bacterial adhesion (LAMBA). LAMBA is programmable and can be functionalized with extracellular moieties up to 545 amino acids. Notably, the adhesion between nanobody-antigen pairs in LAMBA leads to fast recovery under stretching or bending. By exploiting this feature, we fabricated wearable LAMBA sensors that can detect bioelectrical or biomechanical signals. Our work establishes a scalable approach to produce genetically editable and self-healable living functional materials that can be applied in biomanufacturing, bioremediation and soft bioelectronics assembly.

Bottom-up synthetic ecology study of microbial consortia to enhance lignocellulose bioconversion

Lignocellulose is the most abundant organic carbon polymer on the earth. Its decomposition and conversion greatly impact the global carbon cycle. Furthermore, it provides feedstock for sustainable fuel and other value-added products. However, it continues to be underutilized, due to its highly recalcitrant and heterogeneric structure. Microorganisms, which have evolved versatile pathways to convert lignocellulose, undoubtedly are at the heart of lignocellulose conversion. Numerous studies that have reported successful metabolic engineering of individual strains to improve biological lignin valorization. Meanwhile, the bottleneck of single strain modification is becoming increasingly urgent in the conversion of complex substrates. Alternatively, increased attention has been paid to microbial consortia, as they show advantages over pure cultures, e.g., high efficiency and robustness. Here, we first review recent developments in microbial communities for lignocellulose bioconversion. Furthermore, the emerging area of synthetic ecology, which is an integration of synthetic biology, ecology, and computational biology, provides an opportunity for the bottom-up construction of microbial consortia. Then, we review different modes of microbial interaction and their molecular mechanisms, and discuss considerations of how to employ these interactions to construct synthetic consortia via synthetic ecology, as well as highlight emerging trends in engineering microbial communities for lignocellulose bioconversion.

Cheating Promotes Coexistence in a Two-Species One-Substrate Culture Model

Cheating in microbial communities is often regarded as a precursor to a “tragedy of the commons,” ultimately leading to over-exploitation by a few species and destabilization of the community. While current evidence suggests that cheaters are evolutionarily and ecologically abundant, they can also play important roles in communities, such as promoting cooperative behaviors of other species. We developed a closed culture model with two microbial species and a single, complex nutrient substrate (the metaphorical “common”). One of the organisms, an enzyme producer, degrades the substrate, releasing an essential and limiting resource that it can use both to grow and produce more enzymes, but at a cost. The second organism, a cheater, does not produce the enzyme but can access the diffused resource produced by the other species, allowing it to benefit from the public good without contributing to it. We investigated evolutionarily stable states of coexistence between the two organisms and described how enzyme production rates and resource diffusion influence organism abundances. Our model shows that, in the long-term evolutionary scale, monocultures of the producer species drive themselves extinct because selection always favors mutant invaders that invest less in enzyme production, ultimately driving down the release of resources. However, the presence of a cheater buffers this process by reducing the fitness advantage of lower enzyme production, thereby preventing runaway selection in the producer, and promoting coexistence. Resource diffusion rate controls cheater growth, preventing it from outcompeting the producer. These results show that competition from cheaters can force producers to maintain adequate enzyme production to sustain both itself and the cheater. This is similar to what is known in evolutionary game theory as a “snowdrift game” – a metaphor describing a snow shoveler and a cheater following in their clean tracks. We move further to show that cheating can stabilize communities and possibly be a precursor to cooperation, rather than extinction.

Microbial Systems Ecology to Understand Cross-Feeding in Microbiomes

Understanding how microorganism-microorganism interactions shape microbial assemblages is a key to deciphering the evolution of dependencies and co-existence in complex microbiomes. Metabolic dependencies in cross-feeding exist in microbial communities and can at least partially determine microbial community composition. To parry the complexity and experimental limitations caused by the large number of possible interactions, new concepts from systems biology aim to decipher how the components of a system interact with each other. The idea that cross-feeding does impact microbiome assemblages has developed both theoretically and empirically, following a systems biology framework applied to microbial communities, formalized as microbial systems ecology (MSE) and relying on integrated-omics data. This framework merges cellular and community scales and offers new avenues to untangle microbial coexistence primarily by metabolic modeling, one of the main approaches used for mechanistic studies. In this mini-review, we first give a concise explanation of microbial cross-feeding. We then discuss how MSE can enable progress in microbial research. Finally, we provide an overview of a MSE framework mostly based on genome-scale metabolic-network reconstruction that combines top-down and bottom-up approaches to assess the molecular mechanisms of deterministic processes of microbial community assembly that is particularly suitable for use in synthetic biology and microbiome engineering.

Assembling stable syntrophic Escherichia coli communities by comprehensively identifying beneficiaries of secreted goods

Metabolic cross-feeding frequently underlies mutualistic relationships in natural microbial communities and is often exploited to assemble synthetic microbial consortia. We systematically identified all single-gene knockouts suitable for imposing cross-feeding in Escherichia coli and used this information to assemble syntrophic communities. Most strains benefiting from shared goods were dysfunctional in biosynthesis of amino acids, nucleotides, and vitamins or mutants in central carbon metabolism. We tested cross-feeding potency in 1,444 strain pairs and mapped the interaction network between all functional groups of mutants. This network revealed that auxotrophs for vitamins are optimal cooperators. Lastly, we monitored how assemblies composed of dozens of auxotrophs change over time and observed that they rapidly and repeatedly coalesced to seven strain consortia composed primarily from vitamin auxotrophs. The composition of emerging consortia suggests that they were stabilized by multiple cross-feeding interactions. We conclude that vitamins are ideal shared goods since they optimize consortium growth while still imposing member co-dependence.

Two-species community design of lactic acid bacteria for optimal production of lactate

Microbial communities that metabolise pentose and hexose sugars are useful in producing high-value chemicals, resulting in the effective conversion of raw materials to the product, a reduction in the production cost, and increased yield. Here, we present a computational analysis approach called CAMP (Co-culture/Community Analyses for Metabolite Production) that simulates and identifies appropriate communities to produce a metabolite of interest. To demonstrate this approach, we focus on the optimal production of lactate from various Lactic Acid Bacteria. We used genome-scale metabolic models (GSMMs) belonging to Lactobacillus, Leuconostoc, and Pediococcus species from the Virtual Metabolic Human (VMH; https://vmh.life/) resource and well-curated GSMMs of L. plantarum WCSF1 and L. reuteri JCM 1112. We analysed 1176 two-species communities using a constraint-based modelling method for steady-state flux-balance analysis of communities. Flux variability analysis was used to detect the maximum lactate flux in the communities. Using glucose or xylose as substrates separately or in combination resulted in either parasitism, amensalism, or mutualism being the dominant interaction behaviour in the communities. Interaction behaviour between members of the community was deduced based on variations in the predicted growth rates of monocultures and co-cultures. Acetaldehyde, ethanol, acetate, among other metabolites, were found to be cross-fed between community members. L. plantarum WCSF1 was found to be a member of communities with high lactate yields. In silico community optimisation strategies to predict reaction knock-outs for improving lactate flux were implemented. Reaction knock-outs of acetate kinase, phosphate acetyltransferase, and fumarate reductase in the communities were found to enhance lactate production.

OrtSuite: from genomes to prediction of microbial interactions within targeted ecosystem processes

OrtSuite predicts synergistic species interactions using the genomic potential of microbial communities

The high complexity found in microbial communities makes the identification of microbial interactions challenging. To address this challenge, we present OrtSuite, a flexible workflow to predict putative microbial interactions based on genomic content of microbial communities and targeted to specific ecosystem processes. The pipeline is composed of three user-friendly bash commands. OrtSuite combines ortholog clustering with genome annotation strategies limited to user-defined sets of functions allowing for hypothesis-driven data analysis such as assessing microbial interactions in specific ecosystems. OrtSuite matched, on average, 96% of experimentally verified KEGG orthologs involved in benzoate degradation in a known group of benzoate degraders. We evaluated the identification of putative synergistic species interactions using the sequenced genomes of an independent study that had previously proposed potential species interactions in benzoate degradation. OrtSuite is an easy-to-use workflow that allows for rapid functional annotation based on a user-curated database and can easily be extended to ecosystem processes where connections between genes and reactions are known. OrtSuite is an open-source software available at https://github.com/mdsufz/OrtSuite.

Ratio of Electron Donor to Acceptor Influences Metabolic Specialization and Denitrification Dynamics in Pseudomonas aeruginosa in a Mixed Carbon Medium

Denitrifying microbes sequentially reduce nitrate (NO3 -) to nitrite (NO2 -), NO, N2O, and N2 through enzymes encoded by nar, nir, nor, and nos. Some denitrifiers maintain the whole four-gene pathway, but others possess partial pathways. Partial denitrifiers may evolve through metabolic specialization whereas complete denitrifiers may adapt toward greater metabolic flexibility in nitrogen oxide (NOx -) utilization. Both exist within natural environments, but we lack an understanding of selective pressures driving the evolution toward each lifestyle. Here we investigate differences in growth rate, growth yield, denitrification dynamics, and the extent of intermediate metabolite accumulation under varying nutrient conditions between the model complete denitrifier Pseudomonas aeruginosa and a community of engineered specialists with deletions in the denitrification genes nar or nir. Our results in a mixed carbon medium indicate a growth rate vs. yield tradeoff between complete and partial denitrifiers, which varies with total nutrient availability and ratios of organic carbon to NOx -. We found that the cultures of both complete and partial denitrifiers accumulated nitrite and that the metabolic lifestyle coupled with nutrient conditions are responsible for the extent of nitrite accumulation.

Optogenetic Control of Microbial Consortia Populations for Chemical Production

Microbial co-culture fermentations can improve chemical production from complex biosynthetic pathways over monocultures by distributing enzymes across multiple strains, thereby reducing metabolic burden, overcoming endogenous regulatory mechanisms, or exploiting natural traits of different microbial species. However, stabilizing and optimizing microbial subpopulations for maximal chemical production remains a major obstacle in the field. In this study, we demonstrate that optogenetics is an effective strategy to dynamically control populations in microbial co-cultures. Using a new optogenetic circuit we call OptoTA, we regulate an endogenous toxin-antitoxin system, enabling tunability of Escherichia coli growth using only blue light. With this system we can control the population composition of co-cultures of E. coli and Saccharomyces cerevisiae. When introducing in each strain different metabolic modules of biosynthetic pathways for isobutyl acetate or naringenin, we found that the productivity of co-cultures increases by adjusting the population ratios with specific light duty cycles. This study shows the feasibility of using optogenetics to control microbial consortia populations and the advantages of using light to control their chemical production.

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

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

Modelling microbial communities: Harnessing consortia for biotechnological applications

Microbes propagate and thrive in complex communities, and there are many benefits to studying and engineering microbial communities instead of single strains. Microbial communities are being increasingly leveraged in biotechnological applications, as they present significant advantages such as the division of labour and improved substrate utilisation. Nevertheless, they also present some interesting challenges to surmount for the design of efficient biotechnological processes. In this review, we discuss key principles of microbial interactions, followed by a deep dive into genome-scale metabolic models, focussing on a vast repertoire of constraint-based modelling methods that enable us to characterise and understand the metabolic capabilities of microbial communities. Complementary approaches to model microbial communities, such as those based on graph theory, are also briefly discussed. Taken together, these methods provide rich insights into the interactions between microbes and how they influence microbial community productivity. We finally overview approaches that allow us to generate and test numerous synthetic community compositions, followed by tools and methodologies that can predict effective genetic interventions to further improve the productivity of communities. With impending advancements in high-throughput omics of microbial communities, the stage is set for the rapid expansion of microbial community engineering, with a significant impact on biotechnological processes.

Designing microbial communities to maximize the thermodynamic driving force for the production of chemicals

Microbial communities have become a major research focus due to their importance for biogeochemical cycles, biomedicine and biotechnological applications. While some biotechnological applications, such as anaerobic digestion, make use of naturally arising microbial communities, the rational design of microbial consortia for bio-based production processes has recently gained much interest. One class of synthetic microbial consortia is based on specifically designed strains of one species. A common design principle for these consortia is based on division of labor, where the entire production pathway is divided between the different strains to reduce the metabolic burden caused by product synthesis. We first show that classical division of labor does not automatically reduce the metabolic burden when metabolic flux per biomass is analyzed. We then present ASTHERISC (Algorithmic Search of THERmodynamic advantages in Single-species Communities), a new computational approach for designing multi-strain communities of a single-species with the aim to divide a production pathway between different strains such that the thermodynamic driving force for product synthesis is maximized. ASTHERISC exploits the fact that compartmentalization of segments of a product pathway in different strains can circumvent thermodynamic bottlenecks arising when operation of one reaction requires a metabolite with high and operation of another reaction the same metabolite with low concentration. We implemented the ASTHERISC algorithm in a dedicated program package and applied it on E. coli core and genome-scale models with different settings, for example, regarding number of strains or demanded product yield. These calculations showed that, for each scenario, many target metabolites (products) exist where a multi-strain community can provide a thermodynamic advantage compared to a single strain solution. In some cases, a production with sufficiently high yield is thermodynamically only feasible with a community. In summary, the developed ASTHERISC approach provides a promising new principle for designing microbial communities for the bio-based production of chemicals.

Communities of microbes are ubiquitous in nature and also of high relevance for industrial applications, e.g. for the production of biogas. The development and use of non-natural communities for biotechnological applications has become an important subject of research. In this work, we present a new computational method to design synthetic communities with improved capabilities for the synthesis of desired target metabolites. Our method takes a constraint-based metabolic model of an organism as input and searches for a suitable partitioning of the product pathway via different strains of the organism such that the thermodynamic driving force for product synthesis is maximized. Essentially, this approach exploits the fact that having multiple strains allows adjustment of different metabolite concentrations in the different strains by which the thermodynamic driving force for product synthesis can often be increased. We tested this approach with a core and with a genome-scale metabolic network model of Escherichia coli. We found that, for dozens of metabolites, there exist communities with specifically designed strains of E. coli where the maximal thermodynamic driving force can be increased compared to a single E. coli strain. In summary, our presented method provides a new approach, together with a new design principle, for the computational design of microbial communities.