The sum is greater than the parts: exploiting microbial communities to achieve complex functions

Deciphering and designing microbial communities by genome-scale metabolic modelling

Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.

The complex world of kefir: Structural insights and symbiotic relationships

Kefir milk, known for its high nutritional value and health benefits, is traditionally produced by fermenting milk with kefir grains. These grains are a complex symbiotic community of lactic acid bacteria, acetic acid bacteria, yeasts, and other microorganisms. However, the intricate coexistence mechanisms within these microbial colonies remain a mystery, posing challenges in predicting their biological and functional traits. This uncertainty often leads to variability in kefir milk's quality and safety. This review delves into the unique structural characteristics of kefir grains, particularly their distinctive hollow structure. We propose hypotheses on their formation, which appears to be influenced by the aggregation behaviors of the community members and their alliances. In kefir milk, a systematic colonization process is driven by metabolite release, orchestrating the spatiotemporal rearrangement of ecological niches. We place special emphasis on the dynamic spatiotemporal changes within the kefir microbial community. Spatially, we observe variations in species morphology and distribution across different locations within the grain structure. Temporally, the review highlights the succession patterns of the microbial community, shedding light on their evolving interactions.Furthermore, we explore the ecological mechanisms underpinning the formation of a stable community composition. The interplay of cooperative and competitive species within these microorganisms ensures a dynamic balance, contributing to the community's richness and stability. In kefir community, competitive species foster diversity and stability, whereas cooperative species bolster mutualistic symbiosis. By deepening our understanding of the behaviors of these complex microbial communities, we can pave the way for future advancements in the development and diversification of starter cultures for food fermentation processes.

Rational management of the plant microbiome for the Second Green Revolution

The Green Revolution of the mid-20th century transformed agriculture worldwide and has resulted in environmental challenges. A new approach, the Second Green Revolution, seeks to enhance agricultural productivity while minimizing negative environmental impacts. Plant microbiomes play critical roles in plant growth and stress responses, and understanding plant-microbiome interactions is essential for developing sustainable agricultural practices that meet food security and safety challenges, which are among the United Nations Sustainable Development Goals. This review provides a comprehensive exploration of key deterministic processes crucial for developing microbiome management strategies, including the host effect, the facilitator effect, and microbe-microbe interactions. A hierarchical framework for plant microbiome modulation is proposed to bridge the gap between basic research and agricultural applications. This framework emphasizes three levels of modulation: single-strain, synthetic community, and in situ microbiome modulation. Overall, rational management of plant microbiomes has wide-ranging applications in agriculture and can potentially be a core technology for the Second Green Revolution.

Gut microbiota and its therapeutic implications in tumor microenvironment interactions

The development of cancer is not just the growth and proliferation of a single transformed cell, but its tumor microenvironment (TME) also coevolves with it, which is primarily involved in tumor initiation, development, metastasis, and therapeutic responses. Recent years, TME has been emerged as a potential target for cancer diagnosis and treatment. However, the clinical efficacy of treatments targeting the TME, especially its specific components, remains insufficient. In parallel, the gut microbiome is an essential TME component that is crucial in cancer immunotherapy. Thus, assessing and constructing frameworks between the gut microbiota and the TME can significantly enhance the exploration of effective treatment strategies for various tumors. In this review the role of the gut microbiota in human cancers, including its function and relationship with various tumors was summarized. In addition, the interaction between the gut microbiota and the TME as well as its potential applications in cancer therapeutics was described. Furthermore, it was summarized that fecal microbiota transplantation, dietary adjustments, and synthetic biology to introduce gut microbiota-based medical technologies for cancer treatment. This review provides a comprehensive summary for uncovering the mechanism underlying the effects of the gut microbiota on the TME and lays a foundation for the development of personalized medicine in further studies.

Biocontrol in built environments to reduce pathogen exposure and infection risk

The microbiome of the built environment comprises bacterial, archaeal, fungal, and viral communities associated with human-made structures. Even though most of these microbes are benign, antibiotic-resistant pathogens can colonize and emerge indoors, creating infection risk through surface transmission or inhalation. Several studies have catalogued the microbial composition and ecology in different built environment types. These have informed in vitro studies that seek to replicate the physicochemical features that promote pathogenic survival and transmission, ultimately facilitating the development and validation of intervention techniques used to reduce pathogen accumulation. Such interventions include using Bacillus-based cleaning products on surfaces or integrating bacilli into printable materials. Though this work is in its infancy, early research suggests the potential to use microbial biocontrol to reduce hospital- and home-acquired multidrug-resistant infections. Although these techniques hold promise, there is an urgent need to better understand the microbial ecology of built environments and to determine how these biocontrol solutions alter species interactions. This review covers our current understanding of microbial ecology of the built environment and proposes strategies to translate that knowledge into effective biocontrol of antibiotic-resistant pathogens.

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

Making waves: Enhancing pollutant biodegradation via rational engineering of microbial consortia

Biodegradation holds promise as an effective and sustainable process for the removal of synthetic chemical pollutants. Nevertheless, rational engineering of biodegradation for pollutant remediation remains an unfulfilled goal, while chemical pollution of waters and soils continues to advance. Efforts to (i) identify functional bacteria from aquatic and soil microbiomes, (ii) assemble them into biodegrading consortia, and (iii) identify maintenance and performance determinants, are challenged by large number of pollutants and the complexity in the enzymology and ecology of pollutant biodegradation. To overcome these challenges, approaches that leverage knowledge from environmental bio-chem-informatics and metabolic engineering are crucial. Here, we propose a novel high-throughput bio-chem-informatics pipeline, to link chemicals and their predicted biotransformation pathways with potential enzymes and bacterial strains. Our framework systematically selects the most promising candidates for the degradation of chemicals with unknown biotransformation pathways and associated enzymes from the vast array of aquatic and soil bacteria. We substantiated our perspective by validating the pipeline for two chemicals with known or predicted pathways and show that our predicted strains are consistent with strains known to biotransform those chemicals. Such pipelines can be integrated with metabolic network analysis built upon genome-scale models and ecological principles to rationally design fit-for-purpose bacterial communities for augmenting deficient biotransformation functions and study operational and design parameters that influence their structure and function. We believe that research in this direction can pave the way for achieving our long-term goal of enhancing pollutant biodegradation.

Deep insight into oriented propionate production from food waste: Microbiological interpretation and design practice

Using mixed microbial cultures (MMCs) for oriented volatile fatty acids (VFAs) refining in an open environment is a typical challenge due to the microbial diversiform and the process complexity. Especially for carbohydrate-rich waste (such as food waste), butyrate-type fermentation is usually dominant in a single-stage MMCs anaerobic process, while the production of odd-carbon VFAs (such as propionate) is difficult although it plays a significant role in chemicals industries. In this study, firstly, we gave a new perspective on the rationality of the oriented propionate production using MMCs with lactate as feedstock by conducting in-depth microbial informatics and reaction analysis. Secondly, we verified the feasibility of the "food waste-lactate-propionate" route to reverse the original butyrate-type fermentation situation and explore mechanisms for maintaining stability. In the first stage, a defined lactate fermentation microbiome was used to produce lactate-containing broth (80% of total chemical oxygen demand) at pH=4. In the second stage, an undomesticated undefined anaerobic microbiome was used to drive propionate production (45.26% ± 2.23% of total VFAs) under optimized conditions (C/N = 100:1-200:1 and pH=5.0). The low pH environment in the first stage enhanced the lactic acid bacteria to resist the invasion of non-functional flanking bacteria, making the community stable. In the second stage, the system maintained the propionate-type fermentation due to the absence of the ecological niche of the invasive lactic acid bacteria; The selection of propionate-producing specialists was a necessary but not sufficient condition for propionate-type fermentation. At last, this study proposed an enhanced engineering strategy framework for understanding elaborate MMCs fermentation.

Characterizing sediment functional traits and ecological consequences respond to increasing antibiotic pollution

Current studies have shown that the taxonomic structures of ecologically important microbial communities are altered by antibiotic exposure, but the resulting effects on functional potentials and subsequent biogeochemical processes are poorly understood. However, this knowledge is indispensable for developing an accurate projection of nutrient dynamics in the future. Using metagenomic analyses, here we explored the responses of taxonomical and functional structures of a sediment microbial community, and their links with key biogeochemical processes to increasing antibiotic pollution from the pristine inlet to the outfall sites along an aquaculture discharge channel. We identified sharply contrasting sedimentary microbial communities and functional traits along increasing antibiotic pollution. Functional structures exhibited steeper distance-decay relationships than taxonomical structures along both the antibiotic distance and physicochemical distance, revealing higher functional sensitivity. Sediment enzyme activities were significantly and positively coupled with the relative abundances of their coding genes, thus the abundances of genes were indicative of functional potentials. The nitrogen cycling pathways were commonly inhibited by antibiotics, but not for the first step of nitrification, which could synergistically mitigate nitrous oxide emission. However, antibiotic pollution stimulated methanogens and inhibited methanotrophs, thereby promoting methane efflux. Furthermore, microbes could adapt to antibiotic pollution through enriched potential of sulfate uptake. Antibiotics indirectly affected taxonomic structures through alterations in network topological features, which in turn affected sediment functional structures and biogeochemical processes. Notably, only 13 antibiotics concentration-discriminatory genes contributed an overall 95.9% accuracy in diagnosing in situ antibiotic concentrations, in which just two indicators were antibiotic resistance genes. Our study comprehensively integrates sediment compositional and functional traits, biotic interactions, and enzymatic activities, thus generating a better understanding about ecological consequences of increasing antibiotics pollution. KEY POINTS: • Contrasting functional traits respond to increasing antibiotic pollution. • Antibiotics pollution stimulates CH₄ efflux, while mitigating N₂O emission and may drive an adaptive response of enriched sulfate uptake. • Indicator genes contribute 95.9% accuracy in diagnosing antibiotic concentrations.

Guild and Niche Determination Enable Targeted Alteration of the Microbiome

Microbiome science has greatly contributed to our understanding of microbial life and its essential roles for the environment and human health1-5. However, the nature of microbial interactions and how microbial communities respond to perturbations remains poorly understood, resulting in an often descriptive and correlation-based approach to microbiome research6-8. Achieving causal and predictive microbiome science would require direct functional measurements in complex communities to better understand the metabolic role of each member and its interactions with others. In this study we present a new approach that integrates transcription and translation measurements to predict competition and substrate preferences within microbial communities, consequently enabling the selective manipulation of the microbiome. By performing metatranscriptomic (metaRNA-Seq) and metatranslatomic (metaRibo-Seq) analysis in complex samples, we classified microbes into functional groups (i.e. guilds) and demonstrated that members of the same guild are competitors. Furthermore, we predicted preferred substrates based on importer proteins, which specifically benefited selected microbes in the community (i.e. their niche) and simultaneously impaired their competitors. We demonstrated the scalability of microbial guild and niche determination to natural samples and its ability to successfully manipulate microorganisms in complex microbiomes. Thus, the approach enhances the design of pre- and probiotic interventions to selectively alter members within microbial communities, advances our understanding of microbial interactions, and paves the way for establishing causality in microbiome science.

Linking plant functional genes to rhizosphere microbes: a review

The importance of rhizomicrobiome in plant development, nutrition acquisition and stress tolerance is unquestionable. Relevant plant genes corresponding to the above functions also regulate rhizomicrobiome construction. Deciphering the molecular regulatory network of plant-microbe interactions could substantially contribute to improving crop yield and quality. Here, the plant gene-related nutrient uptake, biotic and abiotic stress resistance, which may influence the composition and function of microbial communities, are discussed in this review. In turn, the influence of microbes on the expression of functional plant genes, and thereby plant growth and immunity, is also reviewed. Moreover, we have specifically paid attention to techniques and methods used to link plant functional genes and rhizomicrobiome. Finally, we propose to further explore the molecular mechanisms and signalling pathways of microbe-host gene interactions, which could potentially be used for managing plant health in agricultural systems.

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

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

Microbial cell factories based on filamentous bacteria, yeasts, and fungi

BACKGROUND

Advanced DNA synthesis, biosensor assembly, and genetic circuit development in synthetic biology and metabolic engineering have reinforced the application of filamentous bacteria, yeasts, and fungi as promising chassis cells for chemical production, but their industrial application remains a major challenge that needs to be solved.

RESULTS

As important chassis strains, filamentous microorganisms can synthesize important enzymes, chemicals, and niche pharmaceutical products through microbial fermentation. With the aid of metabolic engineering and synthetic biology, filamentous bacteria, yeasts, and fungi can be developed into efficient microbial cell factories through genome engineering, pathway engineering, tolerance engineering, and microbial engineering. Mutant screening and metabolic engineering can be used in filamentous bacteria, filamentous yeasts (Candida glabrata, Candida utilis), and filamentous fungi (Aspergillus sp., Rhizopus sp.) to greatly increase their capacity for chemical production. This review highlights the potential of using biotechnology to further develop filamentous bacteria, yeasts, and fungi as alternative chassis strains.

CONCLUSIONS

In this review, we recapitulate the recent progress in the application of filamentous bacteria, yeasts, and fungi as microbial cell factories. Furthermore, emphasis on metabolic engineering strategies involved in cellular tolerance, metabolic engineering, and screening are discussed. Finally, we offer an outlook on advanced techniques for the engineering of filamentous bacteria, yeasts, and fungi.

synDNA-a Synthetic DNA Spike-in Method for Absolute Quantification of Shotgun Metagenomic Sequencing

Microbiome studies have the common goal of determining which microbial taxa are present, respond to specific conditions, or promote phenotypic changes in the host. Most of these studies rely on relative abundance measurements to drive conclusions. Inherent limitations of relative values are the inability to determine whether an individual taxon is more or less abundant and the magnitude of this change between the two samples. These limitations can be overcome by using absolute abundance quantifications, which can allow for a more complete understanding of community dynamics by measuring variations in total microbial loads. Obtaining absolute abundance measurements is still technically challenging. Here, we developed synthetic DNA (synDNA) spike-ins that enable precise and cost-effective absolute quantification of microbiome data by adding defined amounts of synDNAs to the samples. We designed 10 synDNAs with the following features: 2,000-bp length, variable GC content (26, 36, 46, 56, or 66% GC), and negligible identity to sequences found in the NCBI database. Dilution pools were generated by mixing the 10 synDNAs at different concentrations. Shotgun metagenomic sequencing showed that the pools of synDNAs with different percentages of GC efficiently reproduced the serial dilution, showing high correlation (r = 0.96; R² ≥ 0.94) and significance (P < 0.01). Furthermore, we demonstrated that the synDNAs can be used as DNA spike-ins to generate linear models and predict with high accuracy the absolute number of bacterial cells in complex microbial communities. IMPORTANCE The synDNAs designed in this study enable accurate and reproducible measurements of absolute amount and fold changes of bacterial species in complex microbial communities. The method proposed here is versatile and promising as it can be applied to bacterial communities or genomic features like genes and operons, in addition to being easily adaptable by other research groups at a low cost. We also made the synDNAs' sequences and the plasmids available to encourage future application of the proposed method in the study of microbial communities.

Engineering Escherichia coli asymmetry distribution-based synthetic consortium for shikimate production

Microbial consortia constitute a promising tool for achieving high-value chemical bio-production. However, customizing the consortium ratio remains challenging. Herein, an asymmetry distribution-based synthetic consortium (ADSC) was developed to switch cell phenotypes using shikimate synthesis for proof of concept. First, the cell pole-organizing protein PopZ was screened as a mediator of asymmetric protein distribution in Escherichia coli. The ADSC was then constructed to incorporate PopZ-mediated asymmetry distribution and a TetR-based transcription repression switch to achieve the dynamical control of microbial population ratio. Finally, the ADSC was used to decouple cell growth from shikimate synthesis by effectively coordinating the ratio of growing cells and production cells at the consortium level, thereby increasing shikimate titer to 30.1 g/L in the 7.5-L bioreactor with a minimal medium. This titer was further improved to 82.5 g/L when using rich medium fermentation. Our results illustrate a novel approach to control consortium structure through ADSC-mediated regulation, highlighting its potential as an efficient strategy for controlling metabolic state in microbes. This article is protected by copyright. All rights reserved.

Quantitative and analytical tools to analyze the spatiotemporal population dynamics of microbial consortia

Microorganisms occupy almost every niche on earth. They play critical roles in maintaining ecological balance, atmospheric C/N cycle, and human health. Microbes live in consortia with metabolite exchange or signal communication. Quantitative and analytical tools are becoming increasingly important to study microbial consortia dynamics. We argue that a combined reductionist and holistic approach will be important to understanding the assembly rules and spatiotemporal population dynamics of the microbial community (MICOM). Reductionism allows us to reconstruct complex MICOM from isolated or simple synthetic consortia. Holism allows us to understand microbes as a community with cooperation and competition. Here we review the recent development of quantitative and analytical tools to uncover the underlying principles in microbial communities that govern their spatiotemporal change and interaction dynamics. Mathematical models and analytical tools will continue to provide essential knowledge and expand our capability to manipulate and control microbial consortia.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Leveraging Experimental Strategies to Capture Different Dimensions of Microbial Interactions

Microorganisms are a fundamental part of virtually every ecosystem on earth. Understanding how collectively they interact, assemble, and function as communities has become a prevalent topic both in fundamental and applied research. Owing to multiple advances in technology, answering questions at the microbial system or network level is now within our grasp. To map and characterize microbial interaction networks, numerous computational approaches have been developed; however, experimentally validating microbial interactions is no trivial task. Microbial interactions are context-dependent, and their complex nature can result in an array of outcomes, not only in terms of fitness or growth, but also in other relevant functions and phenotypes. Thus, approaches to experimentally capture microbial interactions involve a combination of culture methods and phenotypic or functional characterization methods. Here, through our perspective of food microbiologists, we highlight the breadth of innovative and promising experimental strategies for their potential to capture the different dimensions of microbial interactions and their high-throughput application to answer the question; are microbial interaction patterns or network architecture similar along different contextual scales? We further discuss the experimental approaches used to build various types of networks and study their architecture in the context of cell biology and how they translate at the level of microbial ecosystem.

A bioprocess perspective on the production of secondary metabolites by Streptomyces in submerged co-cultures

Filamentous microorganisms are potent sources of bioactive secondary metabolites, the molecules formed in response to complex environmental signals. The chemical diversity encoded in microbial genomes is only partially revealed by following the standard microbiological approaches. Mimicking the natural stimuli through laboratory co-cultivation is one of the most effective methods of awakening the formation of high-value metabolic products. Whereas the biosynthetic outcomes of co-cultures are reviewed extensively, the bioprocess aspects of such efforts are often overlooked. The aim of the present review is to discuss the submerged co-cultivation strategies used for triggering and enhancing secondary metabolites production in Streptomyces, a heavily investigated bacterial genus exhibiting an impressive repertoire of secondary metabolites, including a vast array of antibiotics. The previously published studies on influencing the biosynthetic capabilities of Streptomyces through co-cultivation are comparatively analyzed in the bioprocess perspective, mainly with the focus on the approaches of co-culture initiation, the experimental setup, the design of experimental controls and the ways of influencing the outcomes of co-cultivation processes. These topics are discussed in the general context of secondary metabolites production in submerged microbial co-cultures by referring to the Streptomyces-related studies as illustrative examples.

Kinetic, metabolic, and statistical analytics: addressing metabolic transport limitations among organelles and microbial communities

Microbial organisms engage in a variety of metabolic interactions. A crucial part of these interactions is the exchange of molecules between different organelles, cells, and the environment. The main forces mediating this metabolic exchange are transporters. This transport can be difficult to measure experimentally because several transport mechanisms remain opaque. However, theoretical calculations about the inputs and outputs of cells via metabolic exchanges have enabled the successful inference of the workings of intra-organismal and inter-organismal systems. Kinetic, metabolic, and statistical modeling approaches in combination with omics data are enhancing our knowledge and understanding about metabolic exchange and mass resource allocation. This model-driven analytics approach can guide effective experimental design and yield new insights into biological function and control.

Modelling microbial communities: Harnessing consortia for biotechnological applications

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