Model Microbial Consortia as Tools for Understanding Complex Microbial Communities

Microbial interactions from a new perspective: reinforcement learning reveals new insights into microbiome evolution

MOTIVATION

Microbes are essential part of all ecosystems, influencing material flow and shaping their surroundings. Metabolic modeling has been a useful tool and provided tremendous insights into microbial community metabolism. However, current methods based on flux balance analysis (FBA) usually fail to predict metabolic and regulatory strategies that lead to long-term survival and stability especially in heterogenous communities.

RESULTS

Here, we introduce a novel reinforcement learning algorithm, Self-Playing Microbes in Dynamic FBA, which treats microbial metabolism as a decision-making process, allowing individual microbial agents to evolve by learning and adapting metabolic strategies for enhanced long-term fitness. This algorithm predicts what microbial flux regulation policies will stabilize in the dynamic ecosystem of interest in the presence of other microbes with minimal reliance on predefined strategies. Throughout this article, we present several scenarios wherein our algorithm outperforms existing methods in reproducing outcomes, and we explore the biological significance of these predictions.

AVAILABILITY AND IMPLEMENTATION

The source code for this article is available at: https://github.com/chan-csu/SPAM-DFBA.

Stable States of a Microbial Community Are Formed by Dynamic Metabolic Networks with Members Functioning to Achieve Both Robustness and Plasticity

A more detailed understanding of the mechanisms underlying the formation of microbial communities is essential for the efficient management of microbial ecosystems. The stable states of microbial communities are commonly perceived as static and, thus, have not been extensively examined. The present study investigated stabilizing mechanisms, minority functions, and the reliability of quantitative ana-lyses, emphasizing a metabolic network perspective. A bacterial community, formed by batch transferred cultures supplied with phenol as the sole carbon and energy source and paddy soil as the inoculum, was analyzed using a principal coordinate ana-lysis (PCoA), mathematical models, and quantitative parameters defined as growth activity, community-changing activity, community-forming activity, vulnerable force, and resilience force depending on changes in the abundance of operational taxonomic units (OTUs) using 16S rRNA gene amplicon sequences. PCoA showed succession states until the 3rd transferred cultures and stable states from the 5th to 10th transferred cultures. Quantitative parameters indicated that the bacterial community was dynamic irrespective of the succession and stable states. Three activities fluctuated under stable states. Vulnerable and resilience forces were detected under the succession and stable states, respectively. Mathematical models indicated the construction of metabolic networks, suggesting the stabilizing mechanism of the community structure. Thirteen OTUs coexisted during stable states, and were recognized as core OTUs consisting of majorities, middle-class, and minorities. The abundance of the middle-class changed, whereas that of the others did not, which indicated that core OTUs maintained metabolic networks. Some extremely low abundance OTUs were consistently exchanged, suggesting a role for scavengers. These results indicate that stable states were formed by dynamic metabolic networks with members functioning to achieve robustness and plasticity.

A thermophilic chemolithoautotrophic bacterial consortium suggests a mutual relationship between bacteria in extreme oligotrophic environments

A thermophilic, chemolithoautotrophic, and aerobic microbial consortium (termed carbonitroflex) growing in a nutrient-poor medium and an atmosphere containing N₂, O₂, CO₂, and CO is investigated as a model to expand our understanding of extreme biological systems. Here we show that the consortium is dominated by Carbonactinospora thermoautotrophica (strain StC), followed by Sphaerobacter thermophilus, Chelatococcus spp., and Geobacillus spp. Metagenomic analysis of the consortium reveals a mutual relationship among bacteria, with C. thermoautotrophica StC exhibiting carboxydotrophy and carbon-dioxide storage capacity. C. thermoautotrophica StC, Chelatococcus spp., and S. thermophilus harbor genes encoding CO dehydrogenase and formate oxidase. No pure cultures were obtained under the original growth conditions, indicating that a tightly regulated interactive metabolism might be required for group survival and growth in this extreme oligotrophic system. The breadwinner hypothesis is proposed to explain the metabolic flux model and highlight the vital role of C. thermoautotrophica StC (the sole keystone species and primary carbon producer) in the survival of all consortium members. Our data may contribute to the investigation of complex interactions in extreme environments, exemplifying the interconnections and dependency within microbial communities.

Microbial-Based Products to Control Soil-Borne Pathogens: Methods to Improve Efficacy and to Assess Impacts on Microbiome

Microbial-based products (either as biopesticide or biofertilizers) have a long history of application, though their use is still limited, mainly due to a perceived low and inconsistent efficacy under field conditions. However, their efficacy has always been compared to chemical products, which have a completely different mechanism of action and production process, following the chemical paradigm of agricultural production. This paradigm has also been applied to regulatory processes, particularly for biopesticides, making the marketing of microbial-based formulations difficult. Increased knowledge about bioinocula behavior after application to the soil and their impact on soil microbiome should foster better exploitation of microbial-based products in a complex environment such as the soil. Moreover, the multifunctional capacity of microbial strains with regard to plant growth promotion and protection should also be considered in this respect. Therefore, the methods utilized for these studies are key to improving the knowledge and understanding of microbial-based product activity and improving their efficacy, which, from farmers' point of view, is the parameter to assess the usefulness of a treatment. In this review, we are thus addressing aspects related to the production and formulation process, highlighting the methods that can be used to evaluate the functioning and impact of microbial-based products on soil microbiome, as tools supporting their use and marketing.

Can Microbial Consortium Applications Affect Yield and Quality of Conventionally Managed Processing Tomato?

Three commercial microbial-based biostimulants containing fungi (arbuscular mycorrhizae and Trichoderma spp.) and other microrganisms (plant growth-promoting bacteria and yeasts) were applied on a processing tomato crop in a two-year field experiment in southern Italy. The effects of the growing season and the microorganism-based treatments on the yield, technological traits and functional quality of the tomato fruits were assessed. The year of cultivation affected yield (with a lower fruit weight, higher marketable to total yield ratio and higher percentage of total defective fruits in 2020) and technological components (higher dry matter, titratable acidity, total soluble solids content in 2020). During the first year of the trial, the consortia-based treatments enhanced the soluble solids content (+10.02%) compared to the untreated tomato plants. The sucrose and lycopene content were affected both by the microbial treatments and the growing season (greater values found in 2021 with respect to 2020). The year factor also significantly affected the metabolite content, except for tyrosine, essential (EAA) and branched-chain amino acids (BCAAs). Over the two years of the field trial, FID-consortium enhanced the content of proteins (+53.71%), alanine (+16.55%), aspartic acid (+31.13%), γ-aminobutyric acid (GABA) (+76.51%), glutamine (+55.17%), glycine (+28.13%), monoethanolamine (MEA) (+19.57%), total amino acids (TAA) (+33.55), EAA (+32.56%) and BCAAs (+45.10%) compared to the control. Our findings highlighted the valuable effect of the FID microbial inoculant in boosting several primary metabolites (proteins and amino acids) in the fruits of the processing tomato crop grown under southern Italian environmental conditions, although no effect on the yield and its components was appreciated.

Imbalance in Carbon and Nitrogen Metabolism in Comamonas testosteroni R2 Is Caused by Negative Feedback and Rescued by L-arginine

The collapse of Comamonas testosteroni R2 under chemostat conditions and the aerobic growth of strain R2 under batch conditions with phenol as the sole carbon source were investigated using physiological and transcriptomic techniques. Phenol-/catechol-degrading activities under chemostat conditions gradually decreased, suggesting that metabolites produced from strain R2 accumulated in the culture, which caused negative feedback. The competitive inhibition of phenol hydroxylase and catechol dioxygenase was observed in a crude extract of the supernatant collected from the collapsed culture. Transcriptomic analyses showed that genes related to nitrogen transport were up-regulated; the ammonium transporter amtB was up-regulated approximately 190-fold in the collapsed status, suggesting an increase in the concentration of ammonium in cells. The transcriptional levels of most of the genes related to gluconeogenesis, glycolysis, the pentose phosphate pathway, and the TCA and urea cycles decreased by ~0.7-fold in the stable status, whereas the activities of glutamate synthase and glutamine synthetase increased by ~2-fold. These results suggest that ammonium was assimilated into glutamate and glutamine via 2-oxoglutarate under the limited supply of carbon skeletons, whereas the synthesis of other amino acids and nucleotides was repressed by 0.6-fold. Furthermore, negative feedback appeared to cause an imbalance between carbon and nitrogen metabolism, resulting in collapse. The effects of amino acids on negative feedback were investigated. L-arginine allowed strain R2 to grow normally, even under growth-inhibiting conditions, suggesting that the imbalance was corrected by the stimulation of the urea cycle, resulting in the rescue of strain R2.

Friends or Foes-Microbial Interactions in Nature

Simple Summary

Microorganisms like bacteria, archaea, fungi, microalgae, and viruses mostly form complex interactive networks within the ecosystem rather than existing as single planktonic cells. Interactions among microorganisms occur between the same species, with different species, or even among entirely different genera, families, or even domains. These interactions occur after environmental sensing, followed by converting those signals to molecular and genetic information, including many mechanisms and classes of molecules. Comprehensive studies on microbial interactions disclose key strategies of microbes to colonize and establish in a variety of different environments. Knowledge of the mechanisms involved in the microbial interactions is essential to understand the ecological impact of microbes and the development of dysbioses. It might be the key to exploit strategies and specific agents against different facing challenges, such as chronic and infectious diseases, hunger crisis, pollution, and sustainability.

Abstract

Microorganisms are present in nearly every niche on Earth and mainly do not exist solely but form communities of single or mixed species. Within such microbial populations and between the microbes and a eukaryotic host, various microbial interactions take place in an ever-changing environment. Those microbial interactions are crucial for a successful establishment and maintenance of a microbial population. The basic unit of interaction is the gene expression of each organism in this community in response to biotic or abiotic stimuli. Differential gene expression is responsible for producing exchangeable molecules involved in the interactions, ultimately leading to community behavior. Cooperative and competitive interactions within bacterial communities and between the associated bacteria and the host are the focus of this review, emphasizing microbial cell–cell communication (quorum sensing). Further, metagenomics is discussed as a helpful tool to analyze the complex genomic information of microbial communities and the functional role of different microbes within a community and to identify novel biomolecules for biotechnological applications.

Biotransformation of Doxorubicin Promotes Resilience in Simplified Intestinal Microbial Communities

Chemotherapeutic drugs can cause harmful gastrointestinal side effects, which may be modulated by naturally occurring members of our microbiome. We constructed simplified gut-associated microbial communities to test the hypothesis that bacteria-mediated detoxification of doxorubicin (i.e., a widely used chemotherapeutic) confers protective effects on the human microbiota. Mock communities composed of up to five specific members predicted by genomic analysis to be sensitive to the drug or resistant via biotransformation and/or efflux were grown in vitro over three generational stages to characterize community assembly, response to perturbation (doxorubicin exposure), and resilience. Bacterial growth and drug concentrations were monitored with spectrophotometric assays, and strain relative abundances were evaluated with 16S rRNA gene sequencing. Bacteria with predicted resistance involving biotransformation significantly lowered concentrations of doxorubicin in culture media, permitting growth of drug-sensitive strains in monoculture. Such protective effects were not produced by strains with drug resistance conferred solely by efflux. In the mixed communities, resilience of drug-sensitive members depended on the presence and efficiency of transformers, as well as drug exposure concentration. Fitness of bacteria that were resistant to doxorubicin via efflux, though not transformation, also improved when the transformers were present. Our simplified community uncovered ecological relationships among a dynamic consortium and highlighted drug detoxification by a keystone species. This work may be extended to advance probiotic development that may provide gut-specific protection to patients undergoing cancer treatment. IMPORTANCE While chemotherapy is an essential intervention for treating many forms of cancer, gastrointestinal side effects may precede infections and risks for additional health complications. We developed an in vitro model to characterize key changes in bacterial community dynamics under chemotherapeutic stress and the role of bacterial interactions in drug detoxification to promote microbiota resilience. Our findings have implications for developing bio-based strategies to promote gut health during cancer treatment.

Modeling Growth Kinetics, Interspecies Cell Fusion, and Metabolism of a Clostridium acetobutylicum/Clostridium ljungdahlii Syntrophic Coculture

Clostridium acetobutylicum and Clostridium ljungdahlii grown in a syntrophic culture were recently shown to fuse membranes and exchange cytosolic contents, yielding hybrid cells with significant shifts in gene expression and growth phenotypes. Here, we introduce a dynamic genome-scale metabolic modeling framework to explore how cell fusion alters the growth phenotype and panel of metabolites produced by this binary community. Computational results indicate C. ljungdahlii persists in the coculture through proteome exchange during fusing events, which endow C. ljungdahlii cells with expanded substrate utilization, and access to additional reducing equivalents from C. acetobutylicum-evolved H₂ and through acquisition of C. acetobutylicum-native cofactor-reducing enzymes. Simulations predict maximum theoretical ethanol and isopropanol yields that are increased by 0.64 mmol and 0.39 mmol per mmol hexose sugar consumed, respectively, during exponential growth when cell fusion is active. This modeling effort provides a mechanistic explanation for the metabolic outcome of cellular fusion and altered homeostasis achieved in this syntrophic clostridial community.IMPORTANCE Widespread cell fusion and protein exchange between microbial organisms as observed in synthetic C. acetobutylicum/C. ljungdahlii culture is a novel observation that has not been explored in silico The mechanisms responsible for the observed cell fusion events in this culture are still unknown. In this work, we develop a modeling framework that captures the observed culture composition and metabolic phenotype, use it to offer a mechanistic explanation for how the culture achieves homeostasis, and identify C. ljungdahlii as primary beneficiary of fusion events. The implications for the events described in this study are far reaching, with potential to reshape our understanding of microbial community behavior synthetically and in nature.

Dilution-to-Stimulation/Extinction Method: a Combination Enrichment Strategy To Develop a Minimal and Versatile Lignocellulolytic Bacterial Consortium

The significance of our study mainly lies in the development of a combined top-down enrichment strategy (i.e., dilution to stimulation coupled to dilution to extinction) to build a minimal and versatile lignocellulolytic microbial consortium. We demonstrated that mainly two selectively enriched bacterial species (Pseudomonas sp. and Paenibacillus sp.) are required to drive the effective degradation of plant polymers. Our findings can guide the design of a synthetic bacterial consortium that could improve saccharification (i.e., the release of sugars from agricultural plant residues) processes in biorefineries. In addition, they can help to expand our ecological understanding of plant biomass degradation in enriched bacterial systems.

The engineering of complex communities can be a successful path to understand the ecology of microbial systems and improve biotechnological processes. Here, we developed a strategy to assemble a minimal and effective lignocellulolytic microbial consortium (MELMC) using a sequential combination of dilution-to-stimulation and dilution-to-extinction approaches. The consortium was retrieved from Andean forest soil and selected through incubation in liquid medium with a mixture of three types of agricultural plant residues. After the dilution-to-stimulation phase, approximately 50 bacterial sequence types, mostly belonging to the Sphingobacteriaceae, Enterobacteriaceae, Pseudomonadaceae, and Paenibacillaceae, were significantly enriched. The dilution-to-extinction method demonstrated that only eight of the bacterial sequence types were necessary to maintain microbial growth and plant biomass consumption. After subsequent stabilization, only two bacterial species (Pseudomonas sp. and Paenibacillus sp.) became highly abundant (>99%) within the MELMC, indicating that these are the key players in degradation. Differences in the composition of bacterial communities between biological replicates indicated that selection, sampling, and/or priority effects could shape the consortium structure. The MELMC can degrade up to ∼13% of corn stover, consuming mostly its (hemi)cellulosic fraction. Tests with chromogenic substrates showed that the MELMC secretes an array of endoenzymes able to degrade xylan, arabinoxylan, carboxymethyl cellulose, and wheat straw. Additionally, the metagenomic profile inferred from the phylogenetic composition along with an analysis of carbohydrate-active enzymes of 20 bacterial genomes support the potential of the MELMC to deconstruct plant polysaccharides. This capacity was mainly attributed to the presence of Paenibacillus sp.

IMPORTANCE The significance of our study mainly lies in the development of a combined top-down enrichment strategy (i.e., dilution to stimulation coupled to dilution to extinction) to build a minimal and versatile lignocellulolytic microbial consortium. We demonstrated that mainly two selectively enriched bacterial species (Pseudomonas sp. and Paenibacillus sp.) are required to drive the effective degradation of plant polymers. Our findings can guide the design of a synthetic bacterial consortium that could improve saccharification (i.e., the release of sugars from agricultural plant residues) processes in biorefineries. In addition, they can help to expand our ecological understanding of plant biomass degradation in enriched bacterial systems.

Coexisting mechanisms of bacterial community are changeable even under similar stable conditions in a chemostat culture

The coexisting mechanism of a synthetic bacterial community (SBC) was investigated to better understand how to manage microbial communities. The SBC was constructed with three kinds of phenol-utilizing bacteria, Pseudomonas sp. LAB-08, Comamonas testosteroni R2, and Cupriavidus sp. P-10, under chemostat conditions supplied with phenol as a sole carbon and energy source. Population densities of all strains were monitored by real-time quantitative PCR (qPCR) targeting the gene encoding the large subunit of phenol hydroxylase. Although the supply of phenol was stopped to allow perturbation in the SBC, all of the strains coexisted and the degradation of phenol was maintained for more than 800 days. The qPCR analyses showed that strains LAB-08 and R2 became dominant simultaneously, whereas strain P-10 was a minor population. This phenomenon was observed before and after the phenol-supply stoppage. The kinetic parameters for phenol of the SBC changed before and after the phenol-supply stoppage, which suggests a change in functional roles of strains in the SBC. Transcriptional levels of phenol hydroxylase and catechol dioxygenases of three strains were monitored by reverse-transcription qPCR (RT-qPCR). The RT-qPCR analyses revealed that all strains shared phenol and survived independently before the phenol-supply stoppage. After the stoppage, strain P-10 would incur the cost for degradation of phenol and catechol, whereas strains LAB-08 and R2 seemed to be cheaters using metabolites, indicating the development of the metabolic network. These results indicated that it is important for the management and redesign of microbial communities to understand the metabolism of bacterial communities.

Toward a dynamical understanding of microbial communities

The challenge of moving beyond descriptions of microbial community composition to the point where understanding underlying eco-evolutionary dynamics emerges is daunting. While it is tempting to simplify through use of model communities composed of a small number of types, there is a risk that such strategies fail to capture processes that might be specific and intrinsic to complexity of the community itself. Here, we describe approaches that embrace this complexity and show that, in combination with metagenomic strategies, dynamical insight is increasingly possible. Arising from these studies is mounting evidence of rapid eco-evolutionary change among lineages and a sense that processes, particularly those mediated by horizontal gene transfer, not only are integral to system function, but are central to long-term persistence. That such dynamic, systems-level insight is now possible, means that the study and manipulation of microbial communities can move to new levels of inquiry. This article is part of the theme issue 'Conceptual challenges in microbial community ecology'.

Prediction of Neighbor-Dependent Microbial Interactions From Limited Population Data

Modulation of interspecies interactions by the presence of neighbor species is a key ecological factor that governs dynamics and function of microbial communities, yet the development of theoretical frameworks explicit for understanding context-dependent interactions are still nascent. In a recent study, we proposed a novel rule-based inference method termed the Minimal Interspecies Interaction Adjustment (MIIA) that predicts the reorganization of interaction networks in response to the addition of new species such that the modulation in interaction coefficients caused by additional members is minimal. While the theoretical basis of MIIA was established through the previous work by assuming the full availability of species abundance data in axenic, binary, and complex communities, its extension to actual microbial ecology can be highly constrained in cases that species have not been cultured axenically (e.g., due to their inability to grow in the absence of specific partnerships) because binary interaction coefficients - basic parameters required for implementing the MIIA - are inestimable without axenic and binary population data. Thus, here we present an alternative formulation based on the following two central ideas. First, in the case where only data from axenic cultures are unavailable, we remove axenic populations from governing equations through appropriate scaling. This allows us to predict neighbor-dependent interactions in a relative sense (i.e., fractional change of interactions between with versus without neighbors). Second, in the case where both axenic and binary populations are missing, we parameterize binary interaction coefficients to determine their values through a sensitivity analysis. Through the case study of two microbial communities with distinct characteristics and complexity (i.e., a three-member community where all members can grow independently, and a four-member community that contains member species whose growth is dependent on other species), we demonstrated that despite data limitation, the proposed new formulation was able to successfully predict interspecies interactions that are consistent with experimentally derived results. Therefore, this technical advancement enhances our ability to predict context-dependent interspecies interactions in a broad range of microbial systems without being limited to specific growth conditions as a pre-requisite.

Tracking Replicate Divergence in Microbial Community Composition and Function in Experimental Microcosms

The study of microbial community functions necessitates replicating microbial communities. Variation in community development over time renders this an imperfect process. Thus, anticipating the likely degree of variation among replicate communities may aid in experimental design. We examined divergence in replicate community composition and function among 128 naturally assembled starting communities obtained from soils, each replicated three times, following a 30-day microcosm incubation period. Bacterial and fungal communities diverged in both composition and function among replicates, but remained much more similar to each other than to communities from different starting inocula. Variation in bacterial community composition among replicates was, however, correlated with variation in dissolved organic carbon production. A smaller-scale experiment testing nine starting communities showed that divergence was similar whether replicates were incubated on sterile or non-sterile pine litter, suggesting the impact of a pre-existing community on replicate divergence is minor. However, replicates in this experiment which were incubated for 114 days diverged more than those incubated for 30 days, suggesting experiments that run over long time periods will likely see greater variation among replicate community composition. These results suggest that while replicates diverge at a community level, such divergence is unlikely to severely impede the study of community function.

Meta-Omics- and Metabolic Modeling-Assisted Deciphering of Human Microbiota Metabolism

The human microbiota is a complex community of commensal, symbiotic, and pathogenic microbes that play a crucial role in maintaining the homeostasis of human health. Such a homeostasis is maintained through the collective functioning of enzymatic genes responsible for the production of metabolites, enabling the interaction and signaling within microbiota as well as between microbes and the human host. Understanding microbial genes, their associated chemistries and functions would be valuable for engineering systemic metabolic pathways within the microbiota to manage human health and diseases. Given that there are many unknown gene metabolic functions and interactions, increasing efforts have been made to gain insights into the underlying functions of microbiota metabolism. This can be achieved through culture-independent metagenomic approaches and metabolic modeling to simulate the microenvironment of human microbiota. In this article, the recent advances in metagenome mining and functional profiling for the discovery of the genetic and biochemical links in human microbiota metabolism as well as metabolic modeling for simulation and prediction of metabolic fluxes in the human microbiota are reviewed. This review provides useful insights into the understanding, reconstruction, and modulation of the human microbiota guided by the knowledge acquired from the basic understanding of the human microbiota metabolism.