Metabolic interactions in microbial communities: untangling the Gordian knot

Formation of a constructed microbial community in a nutrient-rich environment indicates bacterial interspecific competition

Understanding the organizational principles of microbial communities is essential for interpreting ecosystem stability. Previous studies have investigated the formation of bacterial communities under nutrient-poor conditions or obligate relationships to observe cooperative interactions among different species. How microorganisms form stabilized communities in nutrient-rich environments, without obligate metabolic interdependency for growth, is still not fully disclosed. In this study, three bacterial strains isolated from the Populus deltoides rhizosphere were co-cultured in complex medium, and their growth behavior was tracked. These strains co-exist in mixed culture over serial transfer for multiple growth-dilution cycles. Competition is proposed as an emergent interaction relationship among the three bacteria based on their significantly decreased growth levels. The effects of different initial inoculum ratios, up to three orders of magnitude, on community structure were investigated, and the final compositions of the mixed communities with various starting composition indicate that community structure is not dependent on the initial inoculum ratio. Furthermore, the competitive relationships within the community were not altered by different initial inoculum ratios. The community structure was simulated by generalized Lotka-Volterra and dynamic flux balance analysis to provide mechanistic predictions into emergence of community structure under a nutrient-rich environment. Metaproteomic analyses provide support for the metabolite exchanges predicted by computational modeling and for highly altered physiologies when microbes are grown in co-culture. These findings broaden our understanding of bacterial community dynamics and metabolic diversity in higher-order interactions and could be significant in the management of rhizospheric bacterial communities.

IMPORTANCE

Bacteria naturally co-exist in multispecies consortia, and the ability to engineer such systems can be useful in biotechnology. Despite this, few studies have been performed to understand how bacteria form a stable community and interact with each other under nutrient-rich conditions. In this study, we investigated the effects of initial inoculum ratios on bacterial community structure using a complex medium and found that the initial inoculum ratio has no significant impact on resultant community structure or on interaction patterns between community members. The microbial population profiles were simulated using computational tools in order to understand intermicrobial relationships and to identify potential metabolic exchanges that occur during stabilization of the bacterial community. Studying microbial community assembly processes is essential for understanding fundamental ecological principles in microbial ecosystems and can be critical in predicting microbial community structure and function.

Modeling Dynamics of Human Gut Microbiota Derived from Gluten Metabolism: Obtention, Maintenance and Characterization of Complex Microbial Communities

Western diets are rich in gluten-containing products, which are frequently poorly digested. The human large intestine harbors microorganisms able to metabolize undigested gluten fragments that have escaped digestion by human enzymatic activities. The aim of this work was obtaining and culturing complex human gut microbial communities derived from gluten metabolism to model the dynamics of healthy human large intestine microbiota associated with different gluten forms. For this purpose, stool samples from six healthy volunteers were inoculated in media containing predigested gluten or predigested gluten plus non-digested gluten. Passages were carried out every 24 h for 15 days in the same medium and community composition along time was studied via V3-V4 16S rDNA sequencing. Diverse microbial communities were successfully obtained. Moreover, communities were shown to be maintained in culture with stable composition for 14 days. Under non-digested gluten presence, communities were enriched in members of Bacillota, such as Lachnospiraceae, Clostridiaceae, Streptococcaceae, Peptoniphilaceae, Selenomonadaceae or Erysipelotrichaceae, and members of Actinomycetota, such as Bifidobacteriaceae and Eggerthellaceae. Contrarily, communities exposed to digested gluten were enriched in Pseudomonadota. Hence, this study shows a method for culture and stable maintenance of gut communities derived from gluten metabolism. This method enables the analysis of microbial metabolism of gluten in the gut from a community perspective.

Where bacteria and eukaryotes meet

The international workshop "Interdisciplinary life of microbes: from single cells to multicellular aggregates," following a virtual preassembly in November 2021, was held in person in Dresden, from 9 to 13 November 2022. It attracted not only prominent experts in biofilm research but also researchers from broadly neighboring disciplines, such as medicine, chemistry, and theoretical and experimental biophysics, both eukaryotic and prokaryotic. Focused brainstorming sessions were the special feature of the event and are at the heart of this commentary.

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.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Synthetic model ecosystem of 12 cryopreservable microbial species allowing for a noninvasive approach

Simultaneous understanding of both population and ecosystem dynamics is crucial in an era marked by the degradation of ecosystem services. Experimental ecosystems are a powerful tool for understanding these dynamics; however, they often face technical challenges, typically falling into two categories: "complex but with limited replicability microcosms" and "highly replicable but overly simplistic microcosms." Herein, we present a high-throughput synthetic microcosm system comprising 12 functionally and phylogenetically diverse microbial species. These species are axenically culturable, cryopreservable, and can be measured noninvasively via microscopy, aided by machine learning. This system includes prokaryotic and eukaryotic producers and decomposers, and eukaryotic consumers to ensure functional redundancy. Our model system exhibited key features of a complex ecosystem: (i) various positive and negative interspecific interactions, (ii) higher-order interactions beyond two-species dynamics, (iii) probabilistic dynamics leading to divergent outcomes, and (iv) stable nonlinear transitions. We identified several conditions under which at least one species from each of the three functional groups-producers, consumers, and decomposers-and one functionally redundant species, persisted for over six months. These conditions set the stage for detailed investigations in the future. Given its designability and experimental replicability, our model ecosystem offers a promising platform for deeper insights integrating both population and ecosystem dynamics.

Advances in modeling techniques for the production and purification of biomolecules: A comprehensive review

In response to the growing demand for therapeutic biomolecules, there is a need for continuous and cost-effective bio-separation techniques to enhance extraction yield and efficiency. Aqueous biphasic extractive fermentation has emerged as an integrated downstream processing technique, offering selective partitioning, high productivity, and preservation of biomolecule integrity. However, the dynamic nature of this technique requires a comprehensive understanding of the underlying separation mechanisms. Unfortunately, the analysis of parameters influencing this dynamic behavior can be challenging due to limited resources and time. To address this, mathematical modeling approaches can be employed to minimize the tedious trial-and-error experimentation process. This review article presents mathematical modeling approaches for both upstream and downstream processing techniques, focusing on the production of biomolecules which can be used in pharmaceutical industries in a cost-effective manner. By leveraging mathematical models, researchers can optimize the production and purification processes, leading to improved efficiency and processing cost reduction in biomolecule production.

The rise of the sourdough: Genome-scale metabolic modeling-based approach to design sourdough starter communities with tailored-made properties

Sourdough starters harbor microbial consortia that benefit the final product's aroma and volume. The complex nature of these spontaneously developed communities raises challenges in predicting the fermentation phenotypes. Herein, we demonstrated for the first time in this field the potential of genome-scale metabolic modeling (GEMs) in the study of sourdough microbial communities. Broad in-silico modeling of microbial growth was applied on communities composed of yeast (Saccharomyces cerevisiae) and different Lactic Acid Bacteria (LAB) species, which mainly predominate in sourdough starters. Simulations of model-represented communities associated specific bacterial compositions with sourdough phenotypes. Based on ranking the phenotypic performances of different combinations, Pediococcus spp. - Lb. sakei group members were predicted to have an optimal effect considering the increase in S. cerevisiae growth abilities and overall CO2 secretion rates. Flux Balance Analysis (FBA) revealed mutual relationships between the Pediococcus spp. - Lb. sakei group members and S. cerevisiae through bidirectional nutrient dependencies, and further underlined that these bacteria compete with the yeast over nutrients to a lesser extent than the rest LAB species. Volatile compounds (VOCs) production was further modeled, identifying species-specific and community-related VOCs production profiles. The in-silico models' predictions were validated by experimentally building synthetic sourdough communities and assessing the fermentation phenotypes. The Pediococcus spp. - Lb. sakei group was indeed associated with increased yeast cell counts and fermentation rates, demonstrating a 25 % increase in the average leavening rates during the first 10 fermentation hours compared to communities with a lower representation of these group members. Overall, these results provide a possible novel strategy towards the de-novo design of sourdough starter communities with tailored-made characterizations, including a shortened leavening period.

Single-cell phenotypes revealed as a key biomarker in bacterial-fungal interactions: a case study of Staphylococcus and Malassezia

IMPORTANCE

Evaluating bacterial-fungal interactions is important for understanding ecological functions in a natural habitat. Many studies have defined bacterial-fungal interactions according to changes in growth rates when co-cultivated. However, the current literature lacks detailed studies on phenotypic changes in single cells associated with transcriptomic profiles to understand the bacterial-fungal interactions. In our study, we measured the single-cell phenotypes of bacteria co-cultivated with fungi using Raman spectroscopy with its transcriptomic profiles and determined the consequence of these interactions in detail. This rapid and reliable phenotyping approach has the potential to provide new insights regarding bacterial-fungal interactions.

Evolution and synthetic biology

Evolutionary observations have often served as an inspiration for biological design. Decoding of the central dogma of life at a molecular level and understanding of the cellular biochemistry have been elegantly used to engineer various synthetic biology applications, including building genetic circuits in vitro and in cells, building synthetic translational systems, and metabolic engineering in cells to biosynthesize and even bioproduce complex high-value molecules. Here, we review three broad areas of synthetic biology that are inspired by evolutionary observations: (i) combinatorial approaches toward cell-based biomolecular evolution, (ii) engineering interdependencies to establish microbial consortia, and (iii) synthetic immunology. In each of the areas, we will highlight the evolutionary premise that was central toward designing these platforms. These are only a subset of the examples where evolution and natural phenomena directly or indirectly serve as a powerful source of inspiration in shaping synthetic biology and biotechnology.

Species' functional traits and interactions drive nitrate-mediated sulfur-oxidizing community structure and functioning

IMPORTANCE

Understanding the processes and mechanisms governing microbial community assembly and their linkages to ecosystem functioning has long been a core issue in microbial ecology. An in-depth insight still requires combining with analyses of species' functional traits and microbial interactions. Our study showed how species' functional traits and interactions determined microbial community structure and functions by a well-controlled laboratory experiment with nitrate-mediated sulfur oxidation systems using high-throughput sequencing and culture-dependent technologies. The results provided solid evidences that species' functional traits and interactions were the intrinsic factors determining community structure and function. More importantly, our study established quantitative links between community structure and function based on species' functional traits and interactions, which would have important implications for the design and synthesis of microbiomes with expected functions.

Syntrophic Interactions Ameliorate Arsenic Inhibition of Solvent-Dechlorinating Dehalococcoides mccartyi

Interactions and nutrient exchanges among members of microbial communities are important for understanding functional relationships in environmental microbiology. We can begin to elucidate the nature of these complex systems by taking a bottom-up approach utilizing simplified, but representative, community members. Here, we assess the effects of a toxic stress event, the addition of arsenite (As(III)), on a syntrophic co-culture containing lactate-fermenting Desulfovibrio vulgaris Hildenborough and solvent-dechlorinating Dehalococcoides mccartyi strain 195. Arsenic and trichloroethene (TCE) are two highly prevalent groundwater contaminants in the United States, and the presence of bioavailable arsenic is of particular concern at remediation sites in which reductive dechlorination has been employed. While we previously showed that low concentrations of arsenite (As(III)) inhibit the keystone TCE-reducing microorganism, D. mccartyi, this study reports the utilization of physiological analysis, transcriptomics, and metabolomics to assess the effects of arsenic on the metabolisms, gene expression, and nutrient exchanges in the described co-culture. It was found that the presence of D. vulgaris ameliorated arsenic stress on D. mccartyi, improving TCE dechlorination under arsenic-contaminated conditions. Nutrient and amino acid export by D. vulgaris may be a stress-ameliorating exchange in this syntrophic co-culture under arsenic stress, based on upregulation of transporters and increased extracellular nutrients like sarcosine and ornithine. These results broaden our knowledge of microbial community interactions and will support the further development and implementation of robust bioremediation strategies at multi-contaminant sites.

Functional analysis of the apple fruit microbiome based on shotgun metagenomic sequencing of conventional and organic orchard samples

Fruits harbour abundant and diverse microbial communities that protect them from post-harvest pathogens. Identification of functional traits associated with a given microbiota can provide a better understanding of their potential influence. Here, we focused on the epiphytic microbiome of apple fruit. We suggest that shotgun metagenomic data can indicate specific functions carried out by different groups and provide information on their potential impact. Samples were collected from the surface of 'Golden Delicious' apples from four orchards that differ in their geographic location and management practice. Approximately 1 million metagenes were predicted based on a high-quality assembly. Functional profiling of the microbiome of fruits from orchards differing in their management practice revealed a functional shift in the microbiota. The organic orchard microbiome was enriched in pathways involved in plant defence activities; the conventional orchard microbiome was enriched in pathways related to the synthesis of antibiotics. The functional significance of the variations was explored using microbial network modelling algorithms to reveal the metabolic role of specific phylogenetic groups. The analysis identified several associations supported by other published studies. For example, the analysis revealed the nutritional dependencies of the Capnodiales group, including the Alternaria pathogen, on aromatic compounds.

Attention Deficit Hyperactivity Disorder (ADHD) and the gut microbiome: An ecological perspective

Attention Deficit Hyperactivity Disorder (ADHD) is an increasingly prevalent neuropsychiatric disorder characterized by hyperactivity, inattention, and impulsivity. Symptoms emerge from underlying deficiencies in neurocircuitry, and recent research has suggested a role played by the gut microbiome. The gut microbiome is an ecosystem of interdependent taxa involved in an exponentially complex web of interactions, plus host gene and reaction pathways, some of which involve neurotransmitters with roles in ADHD neurocircuitry. Studies have analyzed the ADHD gut microbiome using macroscale metrics such as diversity and differential abundance, and have proposed several taxa as elevated or reduced in ADHD compared to Control. Few studies have delved into the complex underlying dynamics ultimately responsible for the emergence of such metrics, leaving a largely incomplete, sometimes contradictory, and ultimately inconclusive picture. We aim to help complete this picture by venturing beyond taxa abundances and into taxa relationships (i.e. cooperation and competition), using a publicly available gut microbiome dataset (targeted 16S, v3-4 region, qPCR) from an observational, case-control study of 30 Control (15 female, 15 male) and 28 ADHD (15 female, 13 male) undergraduate students. We first perform the same macroscale analyses prevalent in ADHD gut microbiome literature (diversity, differential abundance, and composition) to observe the degree of correspondence, or any new trends. We then estimate two-way ecological relationships by producing Control and ADHD Microbial Co-occurrence Networks (MCNs), using SparCC correlations (p ≤ 0.01). We perform community detection to find clusters of taxa estimated to mutually cooperate along with their centroids, and centrality calculations to estimate taxa most vital to overall gut ecology. We finally summarize our results, providing conjectures on how they can guide future experiments, some methods for improving our experiments, and general implications for the field.

A pathogen-specific isotope tracing approach reveals metabolic activities and fluxes of intracellular Salmonella

Pathogenic bacteria proliferating inside mammalian host cells need to rapidly adapt to the intracellular environment. How they achieve this and scavenge essential nutrients from the host has been an open question due to the difficulties in distinguishing between bacterial and host metabolites in situ. Here, we capitalized on the inability of mammalian cells to metabolize mannitol to develop a stable isotopic labeling approach to track Salmonella enterica metabolites during intracellular proliferation in host macrophage and epithelial cells. By measuring label incorporation into Salmonella metabolites with liquid chromatography-mass spectrometry (LC-MS), and combining it with metabolic modeling, we identify relevant carbon sources used by Salmonella, uncover routes of their metabolization, and quantify relative reaction rates in central carbon metabolism. Our results underline the importance of the Entner-Doudoroff pathway (EDP) and the phosphoenolpyruvate carboxylase for intracellularly proliferating Salmonella. More broadly, our metabolic labeling strategy opens novel avenues for understanding the metabolism of pathogens inside host cells.

Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding

When bacterial species with the same resource preferences share the same growth environment, it is commonly believed that direct competition will arise. A large variety of competition and more general 'interaction' models have been formulated, but what is currently lacking are models that link monoculture growth kinetics and community growth under inclusion of emerging biological interactions, such as metabolite cross-feeding. In order to understand and mathematically describe the nature of potential cross-feeding interactions, we design experiments where two bacterial species Pseudomonas putida and Pseudomonas veronii grow in liquid medium either in mono- or as co-culture in a resource-limited environment. We measure population growth under single substrate competition or with double species-specific substrates (substrate 'indifference'), and starting from varying cell ratios of either species. Using experimental data as input, we first consider a mean-field model of resource-based competition, which captures well the empirically observed growth rates for monocultures, but fails to correctly predict growth rates in co-culture mixtures, in particular for skewed starting species ratios. Based on this, we extend the model by cross-feeding interactions where the consumption of substrate by one consumer produces metabolites that in turn are resources for the other consumer, thus leading to positive feedback in the species system. Two different cross-feeding options were considered, which either lead to constant metabolite cross-feeding, or to a regulated form, where metabolite utilization is activated with rates according to either a threshold or a Hill function, dependent on metabolite concentration. Both mathematical proof and experimental data indicate regulated cross-feeding to be the preferred model to constant metabolite utilization, with best co-culture growth predictions in case of high Hill coefficients, close to binary (on/off) activation states. This suggests that species use the appearing metabolite concentrations only when they are becoming high enough; possibly as a consequence of their lower energetic content than the primary substrate. Metabolite sharing was particularly relevant at unbalanced starting cell ratios, causing the minority partner to proliferate more than expected from the competitive substrate because of metabolite release from the majority partner. This effect thus likely quells immediate substrate competition and may be important in natural communities with typical very skewed relative taxa abundances and slower-growing taxa. In conclusion, the regulated bacterial interaction network correctly describes species substrate growth reactions in mixtures with few kinetic parameters that can be obtained from monoculture growth experiments.

Shaping the phycosphere: Analysis of the EPS in diatom-bacterial co-cultures

The phycosphere is a unique niche that fosters complex interactions between microalgae and associated bacteria. The formation of this extracellular environment, and the associated bacterial biodiversity, is heavily influenced by the secretion of extracellular polymers, primarily driven by phototrophic organisms. The exopolysaccharides (EPS) represent the largest fraction of the microalgae-derived exudates, which can be specifically used by heterotrophic bacteria as substrates for metabolic processes. Furthermore, it has been proposed that bacteria and their extracellular factors play a role in both the release and composition of the EPS. In this study, two model microorganisms, the diatom Phaeodactylum tricornutum CCAP 1055/15 and the bacterium Pseudoalteromonas haloplanktis TAC125, were co-cultured in a dual system to assess how their interactions modify the phycosphere chemical composition by analyzing the EPS monosaccharide profile released in the culture media by the two partners. We demonstrate that microalgal-bacterial interactions in this simplified model significantly influenced the architecture of their extracellular environment. We observed that the composition of the exo-environment, as described by the EPS monosaccharide profiles, varied under different culture conditions and times of incubation. This study reports an initial characterization of the molecular modifications occurring in the extracellular environment surrounding two relevant representatives of marine systems.

Local environment, surface characteristics and stochastic processes shape the dynamics of urban dustbin surface microbiome

Dustbins function as critical infrastructures for urban sanitation, creating a distinct breeding ground for microbial assemblages. However, there is no information regarding the dynamics of microbial communities and the underlying mechanism for community assembly on dustbin surfaces. Here, surface samples were collected from three sampling zones (business building, commercial street and residential community) with different types (kitchen waste, harmful waste, recyclables, and others) and materials (metallic and plastic); and distribution pattern and assembly of microbial communities were investigated by high-throughput sequencing. Bacterial and fungal communities showed the distinct community variations across sampling zones and waste sorting. Core community and biomarker species were significantly correlated with the spatial distribution of overall community. The detection of pathogens highlighted the potential risk of surface microbiome. Human skin, human feces and soil biomes were the potential source environments of the surface microbiomes. Neutral model prediction suggested that microbial community assembly was significantly driven by stochastic processes. Co-association patterns varied with sampling zones and waste types, and neutral amplicon sequence variants (ASVs) that fall within the 95 % confidence intervals of neutral model were largely involved in the stability of microbial networks. These findings improve our understanding of the distribution pattern and the underlying assembly of microbial community on the dustbin surface, thus enabling prospective prediction and assessment of urban microbiomes and their impacts on human health.

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.

Construction and Modeling of a Coculture Microplate for Real-Time Measurement of Microbial Interactions

The dynamic structures of microbial communities emerge from the complex network of interactions between their constituent microorganisms. Quantitative measurements of these interactions are important for understanding and engineering ecosystem structure. Here, we present the development and application of the BioMe plate, a redesigned microplate device in which pairs of wells are separated by porous membranes. BioMe facilitates the measurement of dynamic microbial interactions and integrates easily with standard laboratory equipment. We first applied BioMe to recapitulate recently characterized, natural symbiotic interactions between bacteria isolated from the Drosophila melanogaster gut microbiome. Specifically, the BioMe plate allowed us to observe the benefit provided by two Lactobacillus strains to an Acetobacter strain. We next explored the use of BioMe to gain quantitative insight into the engineered obligate syntrophic interaction between a pair of Escherichia coli amino acid auxotrophs. We integrated experimental observations with a mechanistic computational model to quantify key parameters associated with this syntrophic interaction, including metabolite secretion and diffusion rates. This model also allowed us to explain the slow growth observed for auxotrophs growing in adjacent wells by demonstrating that, under the relevant range of parameters, local exchange between auxotrophs is essential for efficient growth. The BioMe plate provides a scalable and flexible approach for the study of dynamic microbial interactions. IMPORTANCE Microbial communities participate in many essential processes from biogeochemical cycles to the maintenance of human health. The structure and functions of these communities are dynamic properties that depend on poorly understood interactions among different species. Unraveling these interactions is therefore a crucial step toward understanding natural microbiota and engineering artificial ones. Microbial interactions have been difficult to measure directly, largely due to limitations of existing methods to disentangle the contribution of different organisms in mixed cocultures. To overcome these limitations, we developed the BioMe plate, a custom microplate-based device that enables direct measurement of microbial interactions, by detecting the abundance of segregated populations of microbes that can exchange small molecules through a membrane. We demonstrated the possible application of the BioMe plate for studying both natural and artificial consortia. BioMe is a scalable and accessible platform that can be used to broadly characterize microbial interactions mediated by diffusible molecules.

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 review of anammox metabolic response to environmental factors: Characteristics and mechanisms

Anaerobic ammonium oxidation (anammox) is a promising low carbon and economic biological nitrogen removal technology. Considering the anammox technology has been easily restricted by environmental factors in practical engineering applications, therefore, it is necessary to understand the metabolic response characteristics of anammox bacteria to different environmental factors, and then guide the application of the anammox process. This review presented the latest advances of the research progress of the effects of different environmental factors on the metabolic pathway of anammox bacteria. The effects as well as mechanisms of conventional environmental factors and emerging pollutants on the anammox metabolic processes were summarized. Also, the role of quorum sensing (QS) mediating the bacteria growth, gene expression and other metabolic process in the anammox system were also reviewed. Finally, interaction and cross-feeding mechanisms of microbial communities in the anammox system were discussed. This review systematically summarized the variations of metabolic mechanism response to the external environment and cross-feeding interactions in the anammox process, which would provide an in-depth understanding for the anammox metabolic process and a comprehensive guidance for future anammox-related metabolic studies and engineering applications.

Previously uncharacterized rectangular bacterial structures in the dolphin mouth

Much remains to be explored regarding the diversity of uncultured, host-associated microbes. Here, we describe rectangular bacterial structures (RBSs) in the mouths of bottlenose dolphins. DNA staining revealed multiple paired bands within RBSs, suggesting the presence of cells dividing along the longitudinal axis. Cryogenic transmission electron microscopy and tomography showed parallel membrane-bound segments that are likely cells, encapsulated by an S-layer-like periodic surface covering. RBSs displayed unusual pilus-like appendages with bundles of threads splayed at the tips. We present multiple lines of evidence, including genomic DNA sequencing of micromanipulated RBSs, 16S rRNA gene sequencing, and fluorescence in situ hybridization, suggesting that RBSs are bacterial and distinct from the genera Simonsiella and Conchiformibius (family Neisseriaceae), with which they share similar morphology and division patterning. Our findings highlight the diversity of novel microbial forms and lifestyles that await characterization using tools complementary to genomics such as microscopy.

Cyanobacteria as cell factories for the photosynthetic production of sucrose

Biofuels and other biologically manufactured sustainable goods are growing in popularity and demand. Carbohydrate feedstocks required for industrial fermentation processes have traditionally been supplied by plant biomass, but the large quantities required to produce replacement commodity products may prevent the long-term feasibility of this approach without alternative strategies to produce sugar feedstocks. Cyanobacteria are under consideration as potential candidates for sustainable production of carbohydrate feedstocks, with potentially lower land and water requirements relative to plants. Several cyanobacterial strains have been genetically engineered to export significant quantities of sugars, especially sucrose. Sucrose is not only naturally synthesized and accumulated by cyanobacteria as a compatible solute to tolerate high salt environments, but also an easily fermentable disaccharide used by many heterotrophic bacteria as a carbon source. In this review, we provide a comprehensive summary of the current knowledge of the endogenous cyanobacterial sucrose synthesis and degradation pathways. We also summarize genetic modifications that have been found to increase sucrose production and secretion. Finally, we consider the current state of synthetic microbial consortia that rely on sugar-secreting cyanobacterial strains, which are co-cultivated alongside heterotrophic microbes able to directly convert the sugars into higher-value compounds (e.g., polyhydroxybutyrates, 3-hydroxypropionic acid, or dyes) in a single-pot reaction. We summarize recent advances reported in such cyanobacteria/heterotroph co-cultivation strategies and provide a perspective on future developments that are likely required to realize their bioindustrial potential.

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.

Bacteriomes in lesions of pulmonary tuberculosis and its association with status of Mycobacterium tuberculosis excretion

BACKGROUND

Bacteria in lung play an important role in sustaining lung health. Understanding the characteristics of bacteriomes in lesions of pulmonary tuberculosis (TB) patients, who excrete Mycobacterium tuberculosis (MTB), is important for TB prevention and effective treatment.  METHODS: In this study, bacteriomes in lesions from TB patients excreting bacteria (TB-E) and those from TB patients not excreting bacteria (TB-NE) with matched normal lung tissues (NT) were compared by 16S rRNA sequencing. Bacterial MetaCyc functions in TB lesions were also predicted by PICRUSt2 tool.

RESULTS

Alpha diversity of bacteria, including Chao 1 and Shannon indexes, for TB-E was significantly higher than those in TB-NE and NT; while for TB-NE group, Chao 1 index was higher than that in NT group. Predominant phyla in TB lesions and NT were Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes, but analysis of similarity (ANOSIM, p < 0.001) revealed significantly different bacterial compositions among TB-E, TB-NE and NT samples. As for bacteriomes in TB lesions, a strong association (ANOSIM, p < 0.001) was observed with the status of MTB excretion. Indicator genera identified in TB-E and TB-NE demonstrated distinctive micro-ecological environments of TB lesions from patients with different clinical manifestations. Co-occurrence analysis revealed a densely-linked bacterial community in TB-NE compared to that in TB-E. MetaCyc functions responsible for menaquinone synthesis and chorismate metabolism that could potentially impact the persistent-state and nutrient metabolism of MTB were enriched in TB-E samples. While in TB-NE samples, enrichment of bacterial MetaCyc function responsible for heme b synthesis might contribute to TB pathology through ferroptosis.

CONCLUSION

Bacteriomes and their MetaCyc functions in TB lesions are elucidated, and they are associated with status of MTB excretion among pulmonary TB patients. These results serve as a basis for designing novel strategies for preventing and treating pulmonary TB disease.

Metabolomic insight into the synergistic mechanism of action of a bacterial consortium in plant growth promotion

It has been established that a consortium consisting of compatible plant growth promoting rhizobacterial strains outperforms their individual impacts on plant attributes. While the phenomenon of synergism is extensively reported, the mechanism that underpins it is yet to be elucidated. In the present study the impact of three plant growth promoting bacteria, Azotobacter chroococcum (A), Priestia megaterium (formerly Bacillus megaterium) (B), and Pseudomonas sp. SK3 (P) was studied as a consortium on the growth attributes of pigeonpea. In addition, microbe-microbe interactions were investigated through metabolomic profiling to understand the mechanism of synergism. Plant growth experiments revealed that bacterial consortium A + B + P showed a significant increase in plant attributes such as shoot length, root length, fresh weight, and dry weight as compared to monocultures and two-membered consortia. Metabolomic profiling through high resolution liquid chromatograph mass spectrometer revealed the presence of a few bioactive compounds in the consortium that might play a potential role in the enhancement of biometric parameters of the plant. Several compounds, such as antipyrine, 6,6-dimethoxy-2,5,5-trimethyl-2-hexene, N-methyltryptamine, 2,2-dimethyl-3,4-bis(4-methoxyphenyl)-2H-1-benzopyran-7-ol acetate, N6-hydroxy-l-lysine, and l-furosin, were detected in the metabolome of the consortium, which was unique among all the treatments. The study also detected a few metabolites involved in sphingolipid biosynthesis (ketosphinganine and sphinganine) known for cell signaling in the consortium. This unravels the possible mechanism of synergism between bacterial strains in a consortium. The metabolomic profile would be helpful to strategically develop unique and more effective consortia that are tailored to the soil type.

Classifying Interactions in a Synthetic Bacterial Community Is Hindered by Inhibitory Growth Medium

Predicting the fate of a microbial community and its member species relies on understanding the nature of their interactions. However, designing simple assays that distinguish between interaction types can be challenging. Here, we performed spent medium assays based on the predictions of a mathematical model to decipher the interactions among four bacterial species: Agrobacterium tumefaciens, Comamonas testosteroni, Microbacterium saperdae, and Ochrobactrum anthropi. While most experimental results matched model predictions, the behavior of C. testosteroni did not: its lag phase was reduced in the pure spent media of A. tumefaciens and M. saperdae but prolonged again when we replenished our growth medium. Further experiments showed that the growth medium actually delayed the growth of C. testosteroni, leading us to suspect that A. tumefaciens and M. saperdae could alleviate this inhibitory effect. There was, however, no evidence supporting such "cross-detoxification," and instead, we identified metabolites secreted by A. tumefaciens and M. saperdae that were then consumed or "cross-fed" by C. testosteroni, shortening its lag phase. Our results highlight that even simple, defined growth media can have inhibitory effects on some species and that such negative effects need to be included in our models. Based on this, we present new guidelines to correctly distinguish between different interaction types such as cross-detoxification and cross-feeding. IMPORTANCE Communities of microbes colonize virtually every place on earth. Ultimately, we strive to predict and control how these communities behave, for example, if they reside in our guts and make us sick. But precise control is impossible unless we can identify exactly how their member species interact with one another. To find a systematic way to measure interactions, we started very simply with a small community of four bacterial species and carefully designed experiments based on a mathematical model. This first attempt accurately mapped out interactions for all species except one. By digging deeper, we understood that our method failed for that species as it was suffering in the growth medium that we chose. A revised model that considered that growth media can be harmful could then make more accurate predictions. What we have learned with these four species can now be applied to decipher interactions in larger communities.

Interspecies Metabolic Interactions in a Synergistic Consortium Drive Efficient Degradation of the Herbicide Bromoxynil Octanoate

Microbial communities play vital roles in biogeochemical cycles, allowing biodegradation of a wide range of pollutants. Although many studies have shown the importance of interspecies interactions on activities of communities, fully elucidating the complex interactions in microbial communities is still challenging. Here, we isolated a consortium containing two bacterial strains (Acinetobacter sp. AG3 and Bacillus sp. R45), which could mineralize bromoxynil octanoate (BO) with higher efficiency than either strain individually. The BO degradation pathway by the synergistic consortium was elucidated, and interspecies interactions in the consortium were explored using genome-scale metabolic models (GSMMs). Modeling showed that growth and degradation enhancements were driven by metabolic interactions, such as syntrophic exchanges of small metabolites in the consortium. Besides, nutritional enhancers were predicted to improve BO degradation, which were tested experimentally. Overall, our results will enhance our understanding of microbial mineralization of BO by consortia and promote the application of microbial communities for bioremediation.

Bioremediation of organic/heavy metal contaminants by mixed cultures of microorganisms: A review

Although microbial remediation has been widely used in the bioremediation of various contaminants, in practical applications of biological remediation, pure cultures of microorganisms are seriously limited by their adaptability, efficiency, and capacity to handle multiple contaminants. Mixed cultures of microorganisms involve the symbiosis of two or more microorganisms. Such cultures exhibit a collection of the characteristics of each microorganism species or strain, showing enormous potential in the bioremediation of organic or heavy metal pollutants. The present review focuses on the mixed cultures of microorganisms, demonstrating its importance and summarizing the advantages of mixed cultures of microorganisms in bioremediation. Furthermore, the internal and external relations of mixed culture microorganisms were analyzed with respect to their involvement in the removal process to elucidate the underlying mechanisms.

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.

Micron-scale biogeography reveals conservative intra anammox bacteria spatial co-associations

Micron-scale resolution can help to reliably identify true taxon-taxon interactions in complex microbial communities. Despite widespread recognition of the critical role of metabolic interactions in anaerobic ammonium oxidation (anammox) system performance, no studies have examined microbial interactions at the micron-scale in anammox consortia. To fill this gap, we extensively sampled (totally 242 samples) the consortia of a lab-scale anammox reactor at different length scales, including bulk-scale (∼cm), macro-scale (300-500 µm) and micron-scale (70-100 µm). We firstly observed evident micron-scale heterogeneity in anammox consortia, with the relative abundance of anammox bacteria fluctuated greatly across individual clusters (2.0%-79.3%), indicating that the biotic interactions play a significant role in the assembly of anammox communities under well-controlled and well-mixed condition. Importantly, by mapping the spatial associations in anammox consortia at micron-scale, we demonstrated that the conserved co-associations for anammox bacteria were restricted to three different Brocadia species over time, and their co-associations with heterotrophs were random, implying that there was no statistically significant symbiotic interaction between anammox bacteria and other heterotrophic populations. Further metagenomic binning revealed that the quorum sensing with secondary messenger c-di-GMP potentially holding on the conservative metabolic cooperation among Brocadia species. These results shed new light on the social behavior of the anammox community. Overall, delineating of biological structures at micron-scale opens a new way of monitoring the microbial spatial structure and interactions, paving the way for improved community engineering of biotreatment systems.

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.

Anaerobic microbial manganese oxidation and reduction: A critical review

Manganese is a vital heavy metal abundant in terrestrial and aquatic environments. Anaerobic manganese redox reactions mediated by microorganisms have been recognized for a long time, which promote elements mobility and bioavailability in the environment. Biological anaerobic redox of manganese serves two reactions, including Mn(II) oxidation and Mn(IV) reduction. This review provides a comprehensive analysis of manganese redox cycles in the environment, closely related to greenhouse gas mitigation, the fate of nutrients, microbial bioremediation, and global biogeochemical cycle, including nitrogen, sulfur, and carbon. The oxidation and reduction of manganese occur cyclically and simultaneously in the environment. Anaerobic reduction of Mn(IV) receives electrons from methane, ammonium and sulfide, while Mn(II) can function as an electron source for manganese-oxidizing microorganisms for autotrophic denitrification and photosynthesis. The anaerobic redox transition between Mn(II) and Mn(IV) promotes a dynamic biogeochemical cycle coupled to microorganisms in water, soil and sediment environments. The discussion of reaction mechanisms, microorganism diversity, environmental influence bioremediation and application identify the research gaps for future investigation, which provides promising opportunities for further development of biotechnological applications to remediate contaminated environments.

Microbial interaction promote the degradation rate of organic matter in thermophilic period

Composting is an efficient, microbe-driven method for the biodegradation of solid organic substrates. In such a complex engineering ecosystem, microbial interaction is more important to function than relative abundance and alpha diversity. However, microbial interaction and its driving force in the composting process has been rarely reported. Thus, we combined network analysis and positive cohesion to analyze the relationship between cooperation among bacteria taxa and the degradation of organic matter in ten industrial-scale food waste composting piles. The results showed that although the complexity of network and microbial diversity were inhibited by high temperature, microbial cooperation was stimulated in the thermophilic period. The positive cohesion, which reflected the degree of microbial cooperation, tended to be positively correlated with the degradation rate of organic matter, functional genera, and genes associated with organic matter degradation. Thus, microbial cooperation was a key factor in the promotion of the degradation of organic matter. From the insight microbial community, Thermobifida was the genera with high abundance, high occurrence frequency, and high contributions to microbial structure. Additionally, it was not only highly associated with the degree of cooperation but was also highly linked with the functional genera in the composting, implying that it might play an important role in regulating cooperation to promote the functional genera. Our research provides a deep understanding of the interaction among bacteria taxa during the composting process. Focusing on the abundance of Thermobifida might be an efficient way to improve composting quality by enhancing the cooperation of microbes.

Rare and localized events stabilize microbial community composition and patterns of spatial self-organization in a fluctuating environment

Spatial self-organization is a hallmark of surface-associated microbial communities that is governed by local environmental conditions and further modified by interspecific interactions. Here, we hypothesize that spatial patterns of microbial cell-types can stabilize the composition of cross-feeding microbial communities under fluctuating environmental conditions. We tested this hypothesis by studying the growth and spatial self-organization of microbial co-cultures consisting of two metabolically interacting strains of the bacterium Pseudomonas stutzeri. We inoculated the co-cultures onto agar surfaces and allowed them to expand (i.e. range expansion) while fluctuating environmental conditions that alter the dependency between the two strains. We alternated between anoxic conditions that induce a mutualistic interaction and oxic conditions that induce a competitive interaction. We observed co-occurrence of both strains in rare and highly localized clusters (referred to as “spatial jackpot events”) that persist during environmental fluctuations. To resolve the underlying mechanisms for the emergence of spatial jackpot events, we used a mechanistic agent-based mathematical model that resolves growth and dispersal at the scale relevant to individual cells. While co-culture composition varied with the strength of the mutualistic interaction and across environmental fluctuations, the model provides insights into the formation of spatially resolved substrate landscapes with localized niches that support the co-occurrence of the two strains and secure co-culture function. This study highlights that in addition to spatial patterns that emerge in response to environmental fluctuations, localized spatial jackpot events ensure persistence of strains across dynamic conditions.

Spectroscopic investigation of faeces with surface-enhanced Raman scattering: a case study with coeliac patients on gluten-free diet

Surface-enhanced Raman scattering (SERS) spectra of faecal samples can be obtained by adding AuNP to their methanol extracts according to the reported protocol, and display bands that are due to bilirubin-like species but also to xanthine and hypoxanthine, two metabolic products secreted by gut bacteria. A total of 27 faecal samples from three different groups, i.e. coeliac patients (n = 9), coeliac patients on gluten-free diet (n = 10) and a control group (n = 8), were characterized with both SERS spectroscopy and 16S rRNA sequencing analysis. Significant differences are present between SERS spectra of coeliac patients and those on gluten-free diet, with a marked increase in the relative intensity of both xanthine and hypoxanthine for the latter. Interestingly, these differences do not correlate with bacterial composition as derived from 16S rRNA sequencing.

Understanding Interaction Patterns within Deep-Sea Microbial Communities and Their Potential Applications

Environmental microbes living in communities engage in complex interspecies interactions that are challenging to decipher. Nevertheless, the interactions provide the basis for shaping community structure and functioning, which is crucial for ecosystem service. In addition, microbial interactions facilitate specific adaptation and ecological evolution processes particularly essential for microbial communities dwelling in resource-limiting habitats, such as the deep oceans. Recent technological and knowledge advancements provide an opportunity for the study of interactions within complex microbial communities, such as those inhabiting deep-sea waters and sediments. The microbial interaction studies provide insights into developing new strategies for biotechnical applications. For example, cooperative microbial interactions drive the degradation of complex organic matter such as chitins and celluloses. Such microbiologically-driven biogeochemical processes stimulate creative designs in many applied sciences. Understanding the interaction processes and mechanisms provides the basis for the development of synthetic communities and consequently the achievement of specific community functions. Microbial community engineering has many application potentials, including the production of novel antibiotics, biofuels, and other valuable chemicals and biomaterials. It can also be developed into biotechniques for waste processing and environmental contaminant bioremediation. This review summarizes our current understanding of the microbial interaction mechanisms and emerging techniques for inferring interactions in deep-sea microbial communities, aiding in future biotechnological and therapeutic applications.

All-In-One: Microbial Response to Natural and Anthropogenic Forcings in a Coastal Mediterranean Ecosystem, the Syracuse Bay (Ionian Sea, Italy)

Bacterial and phytoplankton communities are known to be in close relationships, but how natural and anthropogenic stressors can affect their dynamics is not fully understood. To study the response of microbial communities to environmental and human-induced perturbations, phytoplankton and bacterial communities were seasonally monitored in a Mediterranean coastal ecosystem, Syracuse Bay, where multiple conflicts co-exist. Quali-quantitative, seasonal surveys of the phytoplankton communities (diatoms, dinoflagellates and other taxa), the potential microbial enzymatic activity rates (leucine aminopeptidase, beta-glucosidase and alkaline phosphatase) and heterotrophic culturable bacterial abundance, together with the thermohaline structure and trophic status in terms of nutrient concentrations, phytoplankton biomass (as Chlorophyll-a), and total suspended and particulate organic matter, were carried out. The aim was to integrate microbial community dynamics in the context of the environmental characterization and disentangle microbial patterns related to natural changes from those driven by the anthropic impact on this ecosystem. In spite of the complex relationships between the habitat characteristics, microbial community abundance and metabolic potential, in Syracuse Bay, the availability of organic substrates differently originated by the local conditions appeared to drive the distribution and activity of microbial assemblage. A seasonal pattern of microbial abundances was observed, with the highest concentrations of phytoplankton in spring and low values in winter, whereas heterotrophic bacteria were more abundant during the autumn period. The autumn peaks of the rates of enzymatic activities suggested that not only phytoplankton-derived but also allochthonous organic polymers strongly stimulated microbial metabolism. Increased microbial response in terms of abundance and metabolic activities was detected especially at the sites directly affected by organic matter inputs related to agriculture or aquaculture activities. Nitrogen salts such as nitrate, rather than orthophosphate, were primary drivers of phytoplankton growth. This study also provides insights on the different seasonal scenarios of water quality in Syracuse Bay, which could be helpful for management plans of this Mediterranean coastal environment.

Interspecific competition by non-exoelectrogenic Citrobacter freundii An1 boosts bioelectricity generation of exoelectrogenic Shewanella oneidensis MR-1

The performance of bioelectrochemical systems (BESs) is significantly influenced by metabolic interactions within a particular microbial community. Although some studies show that interspecific metabolic cooperation benefits BESs performance, the effect of interspecific substrate competition on BESs performance has not yet been discussed. Herein, the impact of interspecific competition is investigated by monitoring the extracellular electron transfer of exoelectrogenic Shewanella oneidensis MR-1 and non-exoelectrogenic Citrobacter freundii An1 alone and simultaneously. The bacterial consortia generate the highest current of 38.4 μA cm^-2, 6 times of that produced by the single strain S. oneidensis MR-1. Though S. oneidensis MR-1 loses out to C. freundii An1 in solution, the competition enhances the metabolic activity of S. oneidensis MR-1 on electrode, which facilitates the biofilm formation and therefore helps S. oneidensis MR-1 to gain an competitive advantage over C. freundii An1. Increased metabolic activity triggers more electrons generation and flavin secretion of S. oneidensis MR-1 which contributes to its excellent exoelectrogenic capacity. The proteomics analysis confirms that the expression of proteins related to lactate metabolism, biofilm formation, and outer membrane c-type cytochromes are significantly upregulated in S. oneidensis MR-1 from bacterial consortia.

Metabolic dissimilarity determines the establishment of cross-feeding interactions in bacteria

The exchange of metabolites among different bacterial genotypes profoundly impacts the structure and function of microbial communities. However, the factors governing the establishment of these cross-feeding interactions remain poorly understood. While shared physiological features may facilitate interactions among more closely related individuals, a lower relatedness should reduce competition and thus increase the potential for synergistic interactions. Here, we investigate how the relationship between a metabolite donor and recipient affects the propensity of strains to engage in unidirectional cross-feeding interactions. For this, we performed pairwise cocultivation experiments between four auxotrophic recipients and 25 species of potential amino acid donors. Auxotrophic recipients grew in the vast majority of pairs tested (63%), suggesting metabolic cross-feeding interactions are readily established. Strikingly, both the phylogenetic distance between donor and recipient and the dissimilarity of their metabolic networks were positively associated with the growth of auxotrophic recipients. Analyzing the co-growth of species from a gut microbial community in silico also revealed that recipient genotypes benefitted more from interacting with metabolically dissimilar partners, thus corroborating the empirical results. Together, our work identifies the metabolic dissimilarity between bacterial genotypes as a key factor determining the establishment of metabolic cross-feeding interactions in microbial communities.

Engineering Microbial Consortia towards Bioremediation

Bioremediation is a sustainable remediation technology as it utilizes microorganisms to convert hazardous compounds into their less toxic or non-toxic constituent elements. This technology has achieved some success in the past decades; however, factors involving microbial consortia, such as microbial assembly, functional interactions, and the role of member species, hinder its development. Microbial consortia may be engineered to reconfigure metabolic pathways and reprogram social interactions to get the desired function, thereby providing solutions to its inherent problems. The engineering of microbial consortia is commonly applied for the commercial production of biomolecules. However, in the field of bioremediation, the engineering of microbial consortia needs to be emphasized. In this review, we will discuss the molecular and ecological mechanisms of engineering microbial consortia with a particular focus on metabolic cross-feeding within species and the transfer of metabolites. We also discuss the advantages and limitations of top-down and bottom-up approaches of engineering microbial consortia and their applications in bioremediation.

Metagenomic evidence for a polymicrobial signature of sepsis

Our understanding of the host component of sepsis has made significant progress. However, detailed study of the microorganisms causing sepsis, either as single pathogens or microbial assemblages, has received far less attention. Metagenomic data offer opportunities to characterize the microbial communities found in septic and healthy individuals. In this study we apply gradient-boosted tree classifiers and a novel computational decontamination technique built upon SHapley Additive exPlanations (SHAP) to identify microbial hallmarks which discriminate blood metagenomic samples of septic patients from that of healthy individuals. Classifiers had high performance when using the read assignments to microbial genera [area under the receiver operating characteristic (AUROC=0.995)], including after removal of species ‘culture-confirmed’ as the cause of sepsis through clinical testing (AUROC=0.915). Models trained on single genera were inferior to those employing a polymicrobial model and we identified multiple co-occurring bacterial genera absent from healthy controls. While prevailing diagnostic paradigms seek to identify single pathogens, our results point to the involvement of a polymicrobial community in sepsis. We demonstrate the importance of the microbial component in characterising sepsis, which may offer new biological insights into the aetiology of sepsis, and ultimately support the development of clinical diagnostic or even prognostic tools.

Evolving Interactions and Emergent Functions in Microbial Consortia

Microbial communities are constantly challenged with environmental stressors, such as antimicrobials, pollutants, and global warming. How do they respond to these changes? Answering this question is crucial given that microbial communities perform essential functions for life on Earth. Our research aims to understand and predict communities' responses to change by addressing the following questions. (i) How do eco-evolutionary feedbacks influence microbial community dynamics? (ii) How do multiple interacting species in a microbial community alter evolutionary processes? (iii) To what extent do microbial communities respond to change by ecological versus evolutionary processes? To answer these questions, we use microbial communities of reduced complexity coupled with experimental evolution, genome sequencing, and mathematical modeling. The overall expectation from this integrative research approach is to generate general concepts that extend beyond specific bacterial species and provide fundamental insights into the consequences of evolution on the functioning of whole microbial communities.

NetCom: A Network-Based Tool for Predicting Metabolic Activities of Microbial Communities Based on Interpretation of Metagenomics Data

The study of microbial activity can be viewed as a triangle with three sides: environment (dominant resources in a specific habitat), community (species dictating a repertoire of metabolic conversions) and function (production and/or utilization of resources and compounds). Advances in metagenomics enable a high-resolution description of complex microbial communities in their natural environments and support a systematic study of environment-community-function associations. NetCom is a web-tool for predicting metabolic activities of microbial communities based on network-based interpretation of assembled and annotated metagenomics data. The algorithm takes as an input, lists of differentially abundant enzymatic reactions and generates the following outputs: (i) pathway associations of differently abundant enzymes; (ii) prediction of environmental resources that are unique to each treatment, and their pathway associations; (iii) prediction of compounds that are produced by the microbial community, and pathway association of compounds that are treatment-specific; (iv) network visualization of enzymes, environmental resources and produced compounds, that are treatment specific (2 and 3D). The tool is demonstrated on metagenomic data from rhizosphere and bulk soil samples. By predicting root-specific activities, we illustrate the relevance of our framework for forecasting the impact of soil amendments on the corresponding microbial communities. NetCom is available online.

Optogenetic Tools for Control of Public Goods in Saccharomyces cerevisiae

Microorganisms live in dense and diverse communities, with interactions between cells guiding community development and phenotype. The ability to perturb specific intercellular interactions in space and time provides a powerful route to determining the critical interactions and design rules for microbial communities. Approaches using optogenetic tools to modulate these interactions offer promise, as light can be exquisitely controlled in space and time. We report new plasmids for rapid integration of an optogenetic system into Saccharomyces cerevisiae to engineer light control of expression of a gene of interest. In a proof-of-principle study, we demonstrate the ability to control a model cooperative interaction, namely, the expression of the enzyme invertase (SUC2) which allows S. cerevisiae to hydrolyze sucrose and utilize it as a carbon source. We demonstrate that the strength of this cooperative interaction can be tuned in space and time by modulating light intensity and through spatial control of illumination. Spatial control of light allows cooperators and cheaters to be spatially segregated, and we show that the interplay between cooperative and inhibitory interactions in space can lead to pattern formation. Our strategy can be applied to achieve spatiotemporal control of expression of a gene of interest in S. cerevisiae to perturb both intercellular and interspecies interactions.

IMPORTANCE Recent advances in microbial ecology have highlighted the importance of intercellular interactions in controlling the development, composition, and resilience of microbial communities. In order to better understand the role of these interactions in governing community development, it is critical to be able to alter them in a controlled manner. Optogenetically controlled interactions offer advantages over static perturbations or chemically controlled interactions, as light can be manipulated in space and time and does not require the addition of nutrients or antibiotics. Here, we report a system for rapidly achieving light control of a gene of interest in the important model organism Saccharomyces cerevisiae and demonstrate that by controlling expression of the enzyme invertase, we can control cooperative interactions. This approach will be useful for understanding intercellular and interspecies interactions in natural and synthetic microbial consortia containing S. cerevisiae and serves as a proof of principle for implementing this approach in other consortia.