Electron-transfer-driven spatial optimisation of anaerobic consortia for efficient methanogenesis: Neglected inductive effect of conductive materials.

The inductive effect of conductive materials (CMs) on enhancing methanogenesis metabolism has been overlooked. Herein, we highlight role of CMs in inducing the spatial optimisation of methanogenic consortia by altering the Lewis acid-base (AB) interactions within microbial aggregates. In the presence of CMs and after their removal, the methane production and methane proportion in biogas significantly increase, with no significant difference between the two situations. Analyses of interactions between CMs and extracellular polymer substances (EPSs) with and without D2O reveal that CMs promote release and transfer potential of electron in EPSs, which induce and enhance the role of water molecules being primarily as proton acceptors in the hydrogen bonding between EPSs and water, thereby changing the electron-donor- and electron-acceptor-based AB interactions. Investigations of succession dynamics of microbial communities, co-occurrence networks, and metagenomics further indicate that electron transfer drives the microbial spatial optimisation for efficient methanogenesis through intensive interspecies interactions.

A new bacterial consortia for management of Fusarium head blight in wheat.

Fusarium head blight (FHB) is a significantly important disease in cereals primarily caused by Fusarium species. FHB control is largely executed through chemical strategies, which are costlier to sustainable wheat production, resulting in leaning towards sustainable sources such as resistance breeding and biological control methods for FHB. The present investigation was aimed at evaluating newly identified bacterial consortium (BCM) as biocontrol agents for FHB and understanding the morpho-physiological traits associated with the disease resistance of spring wheat. Preliminary evaluation through antagonistic plate assay and in vivo assessment indicated that BCM effectively inhibited Fusarium growth in spring wheat, reducing area under disease progress curve (AUDPC) and deoxynivalenol (DON), potentially causing type II and V resistance, and improving single spike yield (SSPY). Endurance to FHB infection with the application of BCM is associated with better sustenance of spike photosynthetic performance by improving the light energy harvesting and its utilization. Correlation and path-coefficient analysis indicated that maximum quantum yield (QY_max) is directly influencing the improvement of SSPY and reduction of grain DON accumulation, which is corroborated by principal component analysis. The chlorophyll fluorescence traits identified in the present investigation might be applied as a phenotyping tool for the large-scale identification of wheat sensitivity to FHB.

Metagenomic-based discovery and comparison of the lignin degrading potential of microbiomes in aquatic and terrestrial ecosystems via the LCdb database.

Lignin, as an abundant organic carbon, plays a vital role in the global carbon cycle. However, our understanding of the global lignin-degrading microbiome remains elusive. The greatest barrier has been absence of a comprehensive and accurate functional gene database. Here, we first developed a curated functional gene database (LCdb) for metagenomic profiling of lignin degrading microbial consortia. Via the LCdb, we draw a clear picture describing the global biogeography of communities with lignin-degrading potential. They exhibit clear niche differentiation at the levels of taxonomy and functional traits. The terrestrial microbiomes showed the highest diversity, yet the lowest correlations. In particular, there were few correlations between genes involved in aerobic and anaerobic degradation pathways, showing a clear functional redundancy property. In contrast, enhanced correlations, especially closer inter-connections between anaerobic and aerobic groups, were observed in aquatic consortia in response to the lower diversity. Specifically, dypB and dypA, are widespread on Earth, indicating their essential roles in lignin depolymerization. Estuarine and marine consortia featured the laccase and mnsod genes, respectively. Notably, the roles of archaea in lignin degradation were revealed in marine ecosystems. Environmental factors strongly influenced functional traits, but weakly shaped taxonomic groups. Null mode analysis further verified that composition of functional traits was deterministic, while taxonomic composition was highly stochastic, demonstrating that the environment selects functional genes rather than taxonomic groups. Our study not only develops a useful tool to study lignin degrading microbial communities via metagenome sequencing but also advances our understanding of ecological traits of these global microbiomes.

Synergistic analysis of lignin degrading bacterial consortium and its application in rice straw fiber film.

Fiber film have received widespread attention due to its green friendliness. We can use microorganisms to degrade lignin in straw to obtain cellulose and make fiber films. Herein, a group of high-temperature (50 °C) lignin degrading bacterial consortium (LDH) was enriched and culture conditions for lignin degradation were optimized. Combined with high-throughput sequencing technology, the synergistic effect of LDH-composited bacteria was analyzed. Then LDH was used to treat rice straw for the bio-pulping experiment. The results showed that the lignin of rice straw was degraded 32.4 % by LDH at 50 °C for 10 d, and after the optimization of culture conditions, lignin degradation rate increased by 9.05 % (P < 0.001). The bacteria that compose in LDH can synergistically degrade lignin. Paenibacillus can encode all lignin-degrading enzymes present in the LDH. Preliminary tests of LDH in the pulping industry have been completed. This study is the first to use high temperature lignin degrading bacteria to fabricate fiber film.

Conventional activated sludge vs. photo-sequencing batch reactor for enhanced nitrogen removal in municipal wastewater: Microalgal-bacterial consortium and pathogenic load insights.

Municipal wastewater treatment plants are mostly based on traditional activated sludge (AS) processes. These systems are characterised by major drawbacks: high energy consumption, large amount of excess sludge and high greenhouse gases emissions. Treatment through microalgal-bacterial consortia (MBC) is an alternative and promising solution thanks to lower energy consumption and emissions, biomass production and water sanitation. Here, microbial difference between a traditional anaerobic sludge (AS) and a consortium-based system (photo-sequencing batch reactor (PSBR)) with the same wastewater inlet were characterised through shotgun metagenomics. Stable nitrification was achieved in the PSBR ensuring ammonium removal > 95 % and significant total nitrogen removal thanks to larger flocs enhancing denitrification. The new system showed enhanced pathogen removal, a higher abundance of photosynthetic and denitrifying microorganisms with a reduced emissions potential identifying this novel PSBR as an effective alternative to AS.

Spatially structured microbial consortia and their role in food fermentations.

Microbial consortia are important for the fermentation of foods. They bring combined functionalities to the fermented product, but stability and product consistency of fermentations with complex consortia can be hard to control. Some of these consortia, such as water- and milk-kefir and kombucha, grow as multispecies aggregates or biofilms, in which micro-organisms taking part in a fermentation cascade are spatially organized. The spatial organization of micro-organisms in these aggregates can impact what metabolic interactions are realized in the consortia, ultimately affecting the growth dynamics and evolution of microbes. A better understanding of such spatially structured communities is of interest from the perspective of microbial ecology and biotechnology, as multispecies aggregates can be used to valorize energy-rich substrates, such as plant-based substrates or side streams from the food industry.

Using metabolic networks to predict cross-feeding and competition interactions between microorganisms.

Understanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse properties of bacteria, are currently employed to study these interactions. Recently, the use of metabolic networks to understand the interactions between bacterial pairs has increased, highlighting the relevance of this approach in characterizing bacteria. In this study, we leverage the representation of bacteria through their metabolic networks to build a predictive model aimed at reducing the number of experimental assays required for designing bacterial consortia with specific behaviors. Our novel method for predicting cross-feeding or competition interactions between pairs of microorganisms utilizes metabolic network features. Machine learning classifiers are employed to determine the type of interaction from automatically reconstructed metabolic networks. Several algorithms were assessed and selected based on comprehensive testing and careful separation of manually compiled data sets obtained from literature sources. We used different classification algorithms, including K Nearest Neighbors, XGBoost, Support Vector Machine, and Random Forest, tested different parameter values, and implemented several data curation approaches to reduce the biological bias associated with our data set, ultimately achieving an accuracy of over 0.9. Our method holds substantial potential to advance the understanding of community behavior and contribute to the development of more effective approaches for consortia design.IMPORTANCEUnderstanding bacterial interactions at the community level is critical for microbiology, and leveraging metabolic networks presents an efficient and effective approach. The introduction of this novel method for predicting interactions through machine learning classifiers has the potential to advance the field by reducing the number of experimental assays required and contributing to the development of more effective bacterial consortia.

Microalgal-bacterial consortia for the treatment of livestock wastewater: Removal of pollutants, interaction mechanisms, influencing factors, and prospects for application.

The livestock sector is responsible for a significant amount of wastewater globally. The microalgal-bacterial consortium (MBC) treatment has gained increasing attention as it is able to eliminate pollutants to yield value-added microalgal products. This review offers a critical discussion of the source of pollutants from livestock wastewater and the environmental impact of these pollutants. It also discusses the interactions between microalgae and bacteria in treatment systems and natural habitats in detail. The effects on MBC on the removal of various pollutants (conventional and emerging) are highlighted, focusing specifically on analysis of the removal mechanisms. Notably, the various influencing factors are classified into internal, external, and operating factors, and the mutual feedback relationships between them and the target (removal efficiency and biomass) have been thoroughly analysed. Finally, a wastewater recycling treatment model based on MBC is proposed for the construction of a green livestock farm, and the application value of various microalgal products has been analysed. The overall aim was to indicate that the use of MBC can provide cost-effective and eco-friendly approaches for the treatment of livestock wastewater, thereby advancing the path toward a promising microalgal-bacterial-based technology.

Minorities drive growth resumption in cross-feeding microbial communities.

Microbial communities are fundamental to life on Earth. Different strains within these communities are often connected by a highly connected metabolic network, where the growth of one strain depends on the metabolic activities of other community members. While distributed metabolic functions allow microbes to reduce costs and optimize metabolic pathways, they make them metabolically dependent. Here, we hypothesize that such dependencies can be detrimental in situations where the external conditions change rapidly, as they often do in natural environments. After a shift in external conditions, microbes need to remodel their metabolism, but they can only resume growth once partners on which they depend have also adapted to the new conditions. It is currently not well understood how microbial communities resolve this dilemma and how metabolic interactions are reestablished after an environmental shift. To address this question, we investigated the dynamical responses to environmental perturbation by microbial consortia with distributed anabolic functions. By measuring the regrowth times at the single-cell level in spatially structured communities, we found that metabolic dependencies lead to a growth delay after an environmental shift. However, a minority of cells-those in the immediate neighborhood of their metabolic partners-can regrow quickly and come to numerically dominate the community after the shift. The spatial arrangement of a microbial community is thus a key factor in determining the communities' ability to maintain metabolic interactions and growth in fluctuating conditions. Our results suggest that environmental fluctuations can limit the emergence of metabolic dependencies between microorganisms.

Microbial interactions within beneficial consortia promote soil health.

By ecologically interacting with various biotic and abiotic agents acting in soil ecosystems, highly diverse soil microorganisms establish complex and stable assemblages and survive in a community context in natural settings. Besides facilitating soil microbiome to maintain great levels of population homeostasis, such microbial interactions drive soil microbes to function as the major engine of terrestrial biogeochemical cycling. It is verified that the regulative effect of microbe-microbe interplay plays an instrumental role in microbial-mediated promotion of soil health, including bioremediation of soil pollutants and biocontrol of soil-borne phytopathogens, which is considered an environmentally friendly strategy for ensuring the healthy condition of soils. Specifically, in microbial consortia, it has been proven that microorganism-microorganism interactions are involved in enhancing the soil health-promoting effectiveness (i.e., efficacies of pollution reduction and disease inhibition) of the beneficial microbes, here defined as soil health-promoting agents. These microbial interactions can positively regulate the soil health-enhancing effect by supporting those soil health-promoting agents utilized in combination, as multi-strain soil health-promoting agents, to overcome three main obstacles: inadequate soil colonization, insufficient soil contaminant eradication and inefficient soil-borne pathogen suppression, all of which can restrict their probiotic functionality. Yet the mechanisms underlying such beneficial interaction-related adjustments and how to efficiently assemble soil health-enhancing consortia with the guidance of microbe-microbe communications remain incompletely understood. In this review, we focus on bacterial and fungal soil health-promoting agents to summarize current research progress on the utilization of multi-strain soil health-promoting agents in the control of soil pollution and soil-borne plant diseases. We discuss potential microbial interaction-relevant mechanisms deployed by the probiotic microorganisms to upgrade their functions in managing soil health. We emphasize the interplay-related factors that should be taken into account when building soil health-promoting consortia, and propose a workflow for assembling them by employing a reductionist synthetic community approach.

Top-down and bottom-up cohesiveness in microbial community coalescence.

Microbial communities frequently invade one another as a whole, a phenomenon known as community coalescence. Despite its potential importance for the assembly, dynamics, and stability of microbial consortia, as well as its prospective utility for microbiome engineering, our understanding of the processes that govern it is still very limited. Theory has suggested that microbial communities may exhibit cohesiveness in the face of invasions emerging from collective metabolic interactions across microbes and their environment. This cohesiveness may lead to correlated invasional outcomes, where the fate of a given taxon is determined by that of other members of its community-a hypothesis known as ecological coselection. Here, we have performed over 100 invasion and coalescence experiments with microbial communities of various origins assembled in two different synthetic environments. We show that the dominant members of the primary communities can recruit their rarer partners during coalescence (top-down coselection) and also be recruited by them (bottom-up coselection). With the aid of a consumer-resource model, we found that the emergence of top-down or bottom-up cohesiveness is modulated by the structure of the underlying cross-feeding networks that sustain the coalesced communities. The model also predicts that these two forms of ecological coselection cannot co-occur under our conditions, and we have experimentally confirmed that one can be strong only when the other is weak. Our results provide direct evidence that collective invasions can be expected to produce ecological coselection as a result of cross-feeding interactions at the community level.

Growth Performance, Biochemical Composition and Nutrient Recovery Ability of Twelve Microalgae Consortia Isolated from Various Local Organic Wastes Grown on Nano-Filtered Pig Slurry.

This paper demonstrated the growth ability of twelve algae-microbial consortia (AC) isolated from organic wastes when a pig slurry-derived wastewater (NFP) was used as growth substrate in autotrophic cultivation. Nutrient recovery, biochemical composition, fatty acid and amino acid profiles of algae consortia were evaluated and compared. Three algae-microbial consortia, i.e., a Chlorella-dominated consortium (AC_1), a Tetradesmus and Synechocystis co-dominated consortium (AC_10), and a Chlorella and Tetradesmus co-dominated consortium (AC_12) were found to have the best growth rates (µ of 0.55 ± 0.04, 0.52 ± 0.06, and 0.58 ± 0.03 d-1, respectively), which made them good candidates for further applications. The ACs showed high carbohydrates and lipid contents but low contents of both proteins and essential amino acids, probably because of the low N concentration of NFP. AC_1 and AC_12 showed optimal ω6:ω3 ratios of 3.1 and 3.6, which make them interesting from a nutritional point of view.

Harnessing phytomicrobiome signals for phytopathogenic stress management.

Harnessing the phytomicrobiome offers a great opportunity to improve plant productivity and quality of food. In the recent past, several phytomicrobiome microbes have been explored for their potential involvement in increasing crop yield. This review strategically targets to harness the various dimensions of phytomicrobiome for biotic stress management of crop plants. The tripartite interaction involving plantmicrobiome-pathogen has been discussed. Positive interventions in this system so as to achieve disease tolerant plants has been forayed upon. The different signalling molecules sent out by interacting partners of phytomicrobiome have also been analysed. The novel concept of artificial microbial consortium in mitigation of pathogenic stress has also been touched upon. The aim of this review is to explore the hidden potential of phytomicrobiome diversity as a potent tool against phytopathogens, thereby improving crop health and productivity in a sustainable way.

Development of an Autochthonous Microbial Consortium for Enhanced Bioremediation of PAH-Contaminated Soil.

The main objectives of this study were to isolate bacteria from soil chronically contaminated with polycyclic aromatic hydrocarbons (PAHs), develop an autochthonous microbial consortium, and evaluate its ability to degrade PAHs in their native contaminated soil. Strains with the best bioremediation potential were selected during the multi-stage isolation process. Moreover, to choose bacteria with the highest bioremediation potential, the presence of PAH-degrading genes (pahE) was confirmed and the following tests were performed: tolerance to heavy metals, antagonistic behavior, phytotoxicity, and antimicrobial susceptibility. In vitro degradation of hydrocarbons led to the reduction of the total PAH content by 93.5% after the first day of incubation and by 99.22% after the eighth day. Bioremediation experiment conducted in situ in the contaminated area resulted in the average reduction of the total PAH concentration by 33.3% after 5 months and by over 72% after 13 months, compared to the concentration recorded before the intervention. Therefore, this study implicates that the development of an autochthonous microbial consortium isolated from long-term PAH-contaminated soil has the potential to enhance the bioremediation process.

Electrochemical Technologies to Decrease the Chemical Risk of Hospital Wastewater and Urine.

The inefficiency of conventional biological processes to remove pharmaceutical compounds (PhCs) in wastewater is leading to their accumulation in aquatic environments. These compounds are characterized by high toxicity, high antibiotic activity and low biodegradability, and their presence is causing serious environmental risks. Because much of the PhCs consumed by humans are excreted in the urine, hospital effluents have been considered one of the main routes of entry of PhCs into the environment. In this work, a critical review of the technologies employed for the removal of PhCs in hospital wastewater was carried out. This review provides an overview of the current state of the developed technologies for decreasing the chemical risks associated with the presence of PhCs in hospital wastewater or urine in the last years, including conventional treatments (filtration, adsorption, or biological processes), advanced oxidation processes (AOPs) and electrochemical advanced oxidation processes (EAOPs).

Parsed synthesis of pyocyanin via co-culture enables context-dependent intercellular redox communication.

BACKGROUND: Microbial co-cultures and consortia are of interest in cell-based molecular production and even as "smart" therapeutics in that one can take advantage of division of labor and specialization to expand both the range of available functions and mechanisms for control. The development of tools that enable coordination and modulation of consortia will be crucial for future application of multi-population cultures. In particular, these systems would benefit from an expanded toolset that enables orthogonal inter-strain communication. RESULTS: We created a co-culture for the synthesis of a redox-active phenazine signaling molecule, pyocyanin (PYO), by dividing its synthesis into the generation of its intermediate, phenazine carboxylic acid (PCA) from the first strain, followed by consumption of PCA and generation of PYO in a second strain. Interestingly, both PCA and PYO can be used to actuate gene expression in cells engineered with the soxRS oxidative stress regulon, although importantly this signaling activity was found to depend on growth media. That is, like other signaling motifs in bacterial systems, the signaling activity is context dependent. We then used this co-culture's phenazine signals in a tri-culture to modulate gene expression and production of three model products: quorum sensing molecule autoinducer-1 and two fluorescent marker proteins, eGFP and DsRed. We also showed how these redox-based signals could be intermingled with other quorum-sensing (QS) signals which are more commonly used in synthetic biology, to control complex behaviors. To provide control over product synthesis in the tri-cultures, we also showed how a QS-induced growth control module could guide metabolic flux in one population and at the same time guide overall tri-culture function. Specifically, we showed that phenazine signal recognition, enabled through the oxidative stress response regulon soxRS, was dependent on media composition such that signal propagation within our parsed synthetic system could guide different desired outcomes based on the prevailing environment. In doing so, we expanded the range of signaling molecules available for coordination and the modes by which they can be utilized to influence overall function of a multi-population culture. CONCLUSIONS: Our results show that redox-based signaling can be intermingled with other quorum sensing signaling in ways that enable user-defined control of microbial consortia yielding various outcomes defined by culture medium. Further, we demonstrated the utility of our previously designed growth control module in influencing signal propagation and metabolic activity is unimpeded by orthogonal redox-based signaling. By exploring novel multi-modal strategies for guiding communication and consortia outcome, the concepts introduced here may prove to be useful for coordination of multiple populations within complex microbial systems.

A light tunable differentiation system for the creation and control of consortia in yeast.

Artificial microbial consortia seek to leverage division-of-labour to optimize function and possess immense potential for bioproduction. Co-culturing approaches, the preferred mode of generating a consortium, remain limited in their ability to give rise to stable consortia having finely tuned compositions. Here, we present an artificial differentiation system in budding yeast capable of generating stable microbial consortia with custom functionalities from a single strain at user-defined composition in space and in time based on optogenetically-driven genetic rewiring. Owing to fast, reproducible, and light-tunable dynamics, our system enables dynamic control of consortia composition in continuous cultures for extended periods. We further demonstrate that our system can be extended in a straightforward manner to give rise to consortia with multiple subpopulations. Our artificial differentiation strategy establishes a novel paradigm for the creation of complex microbial consortia that are simple to implement, precisely controllable, and versatile to use.

Emergent spatiotemporal population dynamics with cell-length control of synthetic microbial consortia.

The increased complexity of synthetic microbial biocircuits highlights the need for distributed cell functionality due to concomitant increases in metabolic and regulatory burdens imposed on single-strain topologies. Distributed systems, however, introduce additional challenges since consortium composition and spatiotemporal dynamics of constituent strains must be robustly controlled to achieve desired circuit behaviors. Here, we address these challenges with a modeling-based investigation of emergent spatiotemporal population dynamics using cell-length control in monolayer, two-strain bacterial consortia. We demonstrate that with dynamic control of a strain's division length, nematic cell alignment in close-packed monolayers can be destabilized. We find that this destabilization confers an emergent, competitive advantage to smaller-length strains-but by mechanisms that differ depending on the spatial patterns of the population. We used complementary modeling approaches to elucidate underlying mechanisms: an agent-based model to simulate detailed mechanical and signaling interactions between the competing strains, and a reductive, stochastic lattice model to represent cell-cell interactions with a single rotational parameter. Our modeling suggests that spatial strain-fraction oscillations can be generated when cell-length control is coupled to quorum-sensing signaling in negative feedback topologies. Our research employs novel methods of population control and points the way to programming strain fraction dynamics in consortial synthetic biology.

A zero inflated log-normal model for inference of sparse microbial association networks.

The advent of high-throughput metagenomic sequencing has prompted the development of efficient taxonomic profiling methods allowing to measure the presence, abundance and phylogeny of organisms in a wide range of environmental samples. Multivariate sequence-derived abundance data further has the potential to enable inference of ecological associations between microbial populations, but several technical issues need to be accounted for, like the compositional nature of the data, its extreme sparsity and overdispersion, as well as the frequent need to operate in under-determined regimes. The ecological network reconstruction problem is frequently cast into the paradigm of Gaussian Graphical Models (GGMs) for which efficient structure inference algorithms are available, like the graphical lasso and neighborhood selection. Unfortunately, GGMs or variants thereof can not properly account for the extremely sparse patterns occurring in real-world metagenomic taxonomic profiles. In particular, structural zeros (as opposed to sampling zeros) corresponding to true absences of biological signals fail to be properly handled by most statistical methods. We present here a zero-inflated log-normal graphical model (available at https://github.com/vincentprost/Zi-LN) specifically aimed at handling such "biological" zeros, and demonstrate significant performance gains over state-of-the-art statistical methods for the inference of microbial association networks, with most notable gains obtained when analyzing taxonomic profiles displaying sparsity levels on par with real-world metagenomic datasets.