Microbial metabolic exchange--the chemotype-to-phenotype link

Metabologenomics reveals strain-level genetic and chemical diversity of Microcystis secondary metabolism

Microcystis spp. are renowned for producing the hepatotoxin microcystin in freshwater cyanobacterial harmful algal blooms around the world, threatening drinking water supplies and public and environmental health. However, Microcystis genomes also harbor numerous biosynthetic gene clusters (BGCs) encoding the biosynthesis of other secondary metabolites, including many with toxic properties. Most of these BGCs are uncharacterized and currently lack links to biosynthesis products. However, recent field studies show that many of these BGCs are abundant and transcriptionally active in natural communities, suggesting potentially important yet unknown roles in bloom ecology and water quality. Here, we analyzed 21 xenic Microcystis cultures isolated from western Lake Erie to investigate the diversity of the biosynthetic potential of this genus. Through metabologenomic and in silico approaches, we show that these Microcystis strains contain variable BGCs, previously observed in natural populations, and encode distinct metabolomes across cultures. Additionally, we find that the majority of metabolites and gene clusters are uncharacterized, highlighting our limited understanding of the chemical repertoire of Microcystis spp. Due to the complex metabolomes observed in culture, which contain a wealth of diverse congeners as well as unknown metabolites, these results underscore the need to deeply explore and identify secondary metabolites produced by Microcystis beyond microcystins to assess their impacts on human and environmental health.IMPORTANCEThe genus Microcystis forms dense cyanobacterial harmful algal blooms (cyanoHABs) and can produce the toxin microcystin, which has been responsible for drinking water crises around the world. While microcystins are of great concern, Microcystis also produces an abundance of other secondary metabolites that may be of interest due to their potential for toxicity, ecological importance, or pharmaceutical applications. In this study, we combine genomic and metabolomic approaches to study the genes responsible for the biosynthesis of secondary metabolites as well as the chemical diversity of produced metabolites in Microcystis strains from the Western Lake Erie Culture Collection. This unique collection comprises Microcystis strains that were directly isolated from western Lake Erie, which experiences substantial cyanoHAB events annually and has had negative impacts on drinking water, tourism, and industry.

Metabolomics and Microbial Metabolism: Toward a Systematic Understanding

Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.

Bioactive exometabolites drive maintenance competition in simple bacterial communities

During prolonged resource limitation, bacterial cells can persist in metabolically active states of non-growth. These maintenance periods, such as those experienced in stationary phase, can include upregulation of secondary metabolism and release of exometabolites into the local environment. As resource limitation is common in many environmental microbial habitats, we hypothesized that neighboring bacterial populations employ exometabolites to compete or cooperate during maintenance and that these exometabolite-facilitated interactions can drive community outcomes. Here, we evaluated the consequences of exometabolite interactions over the stationary phase among three environmental strains: Burkholderia thailandensis E264, Chromobacterium subtsugae ATCC 31532, and Pseudomonas syringae pv. tomato DC3000. We assembled them into synthetic communities that only permitted chemical interactions. We compared the responses (transcripts) and outputs (exometabolites) of each member with and without neighbors. We found that transcriptional dynamics were changed with different neighbors and that some of these changes were coordinated between members. The dominant competitor B. thailandensis consistently upregulated biosynthetic gene clusters to produce bioactive exometabolites for both exploitative and interference competition. These results demonstrate that competition strategies during maintenance can contribute to community-level outcomes. It also suggests that the traditional concept of defining competitiveness by growth outcomes may be narrow and that maintenance competition could be an additional or alternative measure.

IMPORTANCE

Free-living microbial populations often persist and engage in environments that offer few or inconsistently available resources. Thus, it is important to investigate microbial interactions in this common and ecologically relevant condition of non-growth. This work investigates the consequences of resource limitation for community metabolic output and for population interactions in simple synthetic bacterial communities. Despite non-growth, we observed active, exometabolite-mediated competition among the bacterial populations. Many of these interactions and produced exometabolites were dependent on the community composition but we also observed that one dominant competitor consistently produced interfering exometabolites regardless. These results are important for predicting and understanding microbial interactions in resource-limited environments.

Microfluidic approaches in microbial ecology

Microbial life is at the heart of many diverse environments and regulates most natural processes, from the functioning of animal organs to the cycling of global carbon. Yet, the study of microbial ecology is often limited by challenges in visualizing microbial processes and replicating the environmental conditions under which they unfold. Microfluidics operates at the characteristic scale at which microorganisms live and perform their functions, thus allowing for the observation and quantification of behaviors such as growth, motility, and responses to external cues, often with greater detail than classical techniques. By enabling a high degree of control in space and time of environmental conditions such as nutrient gradients, pH levels, and fluid flow patterns, microfluidics further provides the opportunity to study microbial processes in conditions that mimic the natural settings harboring microbial life. In this review, we describe how recent applications of microfluidic systems to microbial ecology have enriched our understanding of microbial life and microbial communities. We highlight discoveries enabled by microfluidic approaches ranging from single-cell behaviors to the functioning of multi-cellular communities, and we indicate potential future opportunities to use microfluidics to further advance our understanding of microbial processes and their implications.

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.

Microbial co-culture mediated by intercellular nanotubes during DMAC degradation: Microbial interaction, communication mode, and degradation mechanism

Microbial co-culture has been proven as an effective technique for environmental remediation. In this study, co-culture mechanism of Rhodococcus ruber HJM-8 and Paracoccus communis YBH-X during N,N-dimethylacetamide (DMAC) degradation was studied. The comparison of degradation performance in monoculture and co-culture was presented; due to the efficient cooperation between the two strains via parallel and cascaded degradation, the removal efficiency of total nitrogen (TN) in co-culture could reach 90.1%, which was 1.35 and 1.21 times higher than that of HJM-8 and YBH-X, respectively. Then the communication mode of co-culture during DMAC degradation was determined as contact-independent and contact-dependent interactions between microorganisms. Meanwhile, intercellular nanotube between HJM-8 and YBH-X was found as a unique contact-dependent interaction. The cell staining experiments and RNA sequencing analyses revealed that the nanotube could be used as a bridge to exchange cytoplasmic molecules, and thus improved material transfer and enhanced cell connection in co-culture. The results of KEGG pathway showed that differentially expressed genes in co-culture have an association with cell metabolism, nanotube generation, and genetic material transfer. Furthermore, a mechanism diagram of DMAC biodegradation was proposed for co-culture, indicating that bidirectional cooperation was established between HJM-8 and YBH-X which was mediated by the conversions of acetate and nitrogen. Finally, the co-culture system was validated for treatment of an actual wastewater; results indicated that removal efficiencies of 100% and 68.2% were achieved for DMAC and TN, respectively, suggesting that co-culture had the potential for application.

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

MOTIVATION

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

RESULTS

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

AVAILABILITY AND IMPLEMENTATION

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

Cross-Feeding between Filamentous Cyanobacteria and Symbiotic Bacteria Favors Rapid Photogranulation

Photogranules are dense algal-bacterial aggregates used in aeration-free and carbon-negative wastewater treatment, wherein filamentous cyanobacteria (FC) are essential components. However, little is known about the functional role of symbiotic bacteria in photogranulation. Herein, we combined cyanobacterial isolation, reactor operation, and multiomics analysis to investigate the cyanobacterial-bacterial interaction during photogranulation. The addition of FC to the inoculated sludge achieved a 1.4-fold higher granule size than the control, and the aggregation capacity of FC-dominant photogranules was closely related to the extracellular polysaccharide (PS) concentration (R = 0.86). Importantly, we found that cross-feeding between FC and symbiotic bacteria for macromolecular PS synthesis is at the heart of photogranulation and substantially enhanced the granular stability. Chloroflexi-affiliated bacteria intertwined with FC throughout the photogranules and promoted PS biosynthesis using the partial nucleotide sugars produced by FC. Proteobacteria-affiliated bacteria were spatially close to FC, and highly expressed genes for vitamin B1 and B12 synthesis, contributing the necessary cofactors to promote FC proliferation. In addition, Bacteroidetes-affiliated bacteria degraded FC-derived carbohydrates and influenced granules development. Our metabolic characterization identified the functional role of symbiotic bacteria of FC during photogranulation and shed light on the critical cyanobacterial-bacterial interactions in photogranules from the viewpoint of cross-feeding.

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.

Targeting the human gut microbiome with small-molecule inhibitors

The human gut microbiome is a complex microbial community that is strongly linked to both host health and disease. However, the detailed molecular mechanisms underlying the effects of these microorganisms on host biology remain largely uncharacterized. The development of non-lethal, small-molecule inhibitors that target specific gut microbial activities enables a powerful but underutilized approach to studying the gut microbiome and a promising therapeutic strategy. In this Review, we will discuss the challenges of studying this microbial community, the historic use of small-molecule inhibitors in microbial ecology, and recent applications of this strategy. We also discuss the evidence suggesting that host-targeted drugs can affect the growth and metabolism of gut microbes. Finally, we address the issues of developing and implementing microbiome-targeted small-molecule inhibitors and define important future directions for this research.

Impact of direct physical association and motility on fitness of a synthetic interkingdom microbial community

Mutualistic exchange of metabolites can play an important role in microbial communities. Under natural environmental conditions, such exchange may be compromised by the dispersal of metabolites and by the presence of non-cooperating microorganisms. Spatial proximity between members during sessile growth on solid surfaces has been shown to promote stabilization of cross-feeding communities against these challenges. Nonetheless, many natural cross-feeding communities are not sessile but rather pelagic and exist in turbulent aquatic environments, where partner proximity is often achieved via direct cell-cell adhesion, and cooperation occurs between physically associated cells. Partner association in aquatic environments could be further enhanced by motility of individual planktonic microorganisms. In this work, we establish a model bipartite cross-feeding community between bacteria and yeast auxotrophs to investigate the impact of direct adhesion between prokaryotic and eukaryotic partners and of bacterial motility in a stirred mutualistic co-culture. We demonstrate that adhesion can provide fitness benefit to the bacterial partner, likely by enabling local metabolite exchange within co-aggregates, and that it counteracts invasion of the community by a non-cooperating cheater strain. In a turbulent environment and at low cell densities, fitness of the bacterial partner and its competitiveness against a non-cooperating strain are further increased by motility that likely facilitates partner encounters and adhesion. These results suggest that, despite their potential fitness costs, direct adhesion between partners and its enhancement by motility may play key roles as stabilization factors for metabolic communities in turbulent aquatic environments.

New Technological Approaches for Dental Caries Treatment: From Liquid Crystalline Systems to Nanocarriers

Dental caries is the most common oral disease, with high prevalence rates in adolescents and low-income and lower-middle-income countries. This disease originates from acid production by bacteria, leading to demineralization of the dental enamel and the formation of cavities. The treatment of caries remains a global challenge and the development of effective drug delivery systems is a potential strategy. In this context, different drug delivery systems have been investigated to remove oral biofilms and remineralize dental enamel. For a successful application of these systems, it is necessary that they remain adhered to the surfaces of the teeth to allow enough time for the removal of biofilms and enamel remineralization, thus, the use of mucoadhesive systems is highly encouraged. Among the systems used for this purpose, liquid crystalline systems, polymer-based nanoparticles, lipid-based nanoparticles, and inorganic nanoparticles have demonstrated great potential for preventing and treating dental caries through their own antimicrobial and remineralization properties or through delivering drugs. Therefore, the present review addresses the main drug delivery systems investigated in the treatment and prevention of dental caries.

Stable isotope-assisted metabolite profiling reveals new insights into L-tryptophan chemotrophic metabolism of Rubrivivax benzoatilyticus

Anoxygenic photosynthetic bacteria (APB) are metabolically versatile, capable of surviving with an extended range of carbon and nitrogen sources. This group of phototrophic bacteria have remarkable metabolic plasticity in utilizing an array of organic compounds as carbon source/electron donors and nitrogen sources with sophisticated growth modes. Rubrivivax benzoatilyticus JA2 is one such photosynthetic bacterium utilizes L-tryptophan as nitrogen source under phototrophic growth mode and produces an array of indolic compounds of biotechnological significance. However, chemotrophic L-tryptophan metabolism is largely unexplored and studying L-tryptophan metabolism under chemotrophic mode would provide new insights into metabolic potential of strain JA2. In the present study, we employed stable-isotopes assisted metabolite profiling to unravel the L-tryptophan catabolism in Rubrivivax benzoatilyticus strain JA2 under chemotrophic (dark aerobic) conditions. Utilization of L-tryptophan as a nitrogen source for growth and simultaneous production of indole derivatives was observed in strain JA2. Liquid chromatography mass spectrometry (LC-MS) analysis of exo-metabolite profiling of carbon labeled L-tryptophan (¹³C₁₁) fed cultures of strain JA2 revealed at least seventy labeled metabolites. Of these, only fourteen metabolites were confirmed using standards, while sixteen were putative and forty metabolites remained unidentified. L-tryptophan chemotrophic catabolism revealed multiple catabolic pathways and distinct differential catabolism of L-tryptophan under chemotropic state as compared to photo-catabolism of L-tryptophan in strain JA2.

Functional modulation of chemical mediators in microbial communities

Interactions between microorganisms are often mediated by specialized metabolites. Although the structures and biosynthesis of these compounds may have been elucidated, microbes exist within complex microbiomes and chemical signals can thus also be subject to community-dependent modifications. Increasingly powerful chemical and biological tools allow to shed light on this poorly understood aspect of chemical ecology. We provide an overview of loss-of-function and gain-of-function chemical mediator (CM) modifications within microbial multipartner relationships. Although loss-of-function modifications are abundant in the literature, few gain-of-function modifications have been described despite their important role in microbial interactions. Research in this field holds great potential for our understanding of microbial interactions and may also provide novel tools for targeted interference with microbial signaling.

Towards the human nasal microbiome: Simulating D. pigrum and S. aureus

The human nose harbors various microbes that decisively influence the wellbeing and health of their host. Among the most threatening pathogens in this habitat is Staphylococcus aureus. Multiple epidemiological studies identify Dolosigranulum pigrum as a likely beneficial bacterium based on its positive association with health, including negative associations with S. aureus. Carefully curated GEMs are available for both bacterial species that reliably simulate their growth behavior in isolation. To unravel the mutual effects among bacteria, building community models for simulating co-culture growth is necessary. However, modeling microbial communities remains challenging. This article illustrates how applying the NCMW fosters our understanding of two microbes' joint growth conditions in the nasal habitat and their intricate interplay from a metabolic modeling perspective. The resulting community model combines the latest available curated GEMs of D. pigrum and S. aureus. This uses case illustrates how to incorporate genuine GEM of participating microorganisms and creates a basic community model mimicking the human nasal environment. Our analysis supports the role of negative microbe-microbe interactions involving D. pigrum examined experimentally in the lab. By this, we identify and characterize metabolic exchange factors involved in a specific interaction between D. pigrum and S. aureus as an in silico candidate factor for a deep insight into the associated species. This method may serve as a blueprint for developing more complex microbial interaction models. Its direct application suggests new ways to prevent disease-causing infections by inhibiting the growth of pathogens such as S. aureus through microbe-microbe interactions.

Proteobacteria and Firmicutes Secreted Factors Exert Distinct Effects on Pseudomonas aeruginosa Infection under Normoxia or Mild Hypoxia

Microbiota may alter a pathogen's virulence potential at polymicrobial infection sites. Here, we developed a multi-modal Drosophila assay, amenable to the assessment of human bacterial interactions using fly survival or midgut regeneration as a readout, under normoxia or mild hypoxia. Deploying a matrix of 12 by 33 one-to-one Drosophila co-infections via feeding, we classified bacterial interactions as neutral, synergistic, or antagonistic, based on fly survival. Twenty six percent of these interactions were antagonistic, mainly occurring between Proteobacteria. Specifically, Pseudomonas aeruginosa infection was antagonized by various Klebsiella strains, Acinetobacter baumannii, and Escherichia coli. We validated these interactions in a second screen of 7 by 34 one-to-one Drosophila co-infections based on assessments of midgut regeneration, and in bacterial co-culture test tube assays, where antagonistic interactions depended on secreted factors produced upon high sugar availability. Moreover, Enterococci interacted synergistically with P. aeruginosa in flies and in test tubes, enhancing the virulence and pyocyanin production by P. aeruginosa. However, neither lactic acid bacteria nor their severely hypoxic culture supernatants provided a survival benefit upon P. aeruginosa infection of flies or mice, respectively. We propose that at normoxic or mildly hypoxic sites, Firmicutes may exacerbate, whereas Proteobacteria secreted factors may ameliorate, P. aeruginosa infections.

Engineering bacterial surface interactions using DNA as a programmable material

The diverse surface interactions and functions of a bacterium play an important role in cell signaling, host infection, and colony formation. To understand and synthetically control the biological functions of individual cells as well as the whole community, there is growing attention on the development of chemical and biological tools that can integrate artificial functional motifs onto the bacterial surface to replace the native interactions, enabling a variety of applications in biosynthesis, environmental protection, and human health. Among all these functional motifs, DNA emerges as a powerful tool that can precisely control bacterial interactions at the bio-interface due to its programmability and biorecognition properties. Compared with conventional chemical and genetic approaches, the sequence-specific Watson-Crick interaction enables almost unlimited programmability in DNA nanostructures, realizing one base-pair spatial control and bio-responsive properties. This highlight aims to provide an overview on this emerging research topic of DNA-engineered bacterial interactions from the aspect of synthetic chemists. We start with the introduction of native bacterial surface ligands and established synthetic approaches to install artificial ligands, including direct modification, metabolic engineering, and genetic engineering. A brief overview of DNA nanotechnology, reported DNA-bacteria conjugation chemistries, and several examples of DNA-engineered bacteria are included in this highlight. The future perspectives and challenges in this field are also discussed, including the development of dynamic bacterial surface chemistry, assembly of programmable multicellular community, and realization of bacteria-based theranostic agents and synthetic microbiota as long-term goals.

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

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

Microorganisms: the secret agents of the biosphere, and their key roles in biotechnology

Abstract We present a survey of projects that have been funded by FAPESP under the BIOTA-Microorganisms program. These projects generated a wide variety of results, including the identification of novel antibacterial-producing microorganisms, the characterization of novel microbial enzymes for industrial applications, taxonomic classification of novel microorganisms in several environments, investigation of the soil and mangrove microbial ecosystems and its influence on endangered plant species, and the sequencing of novel metagenome-assembled genomes. The results surveyed demonstrate the importance of microorganisms in environments that play important roles in human activities as well as the potential that many of these microorganisms have in contributing to biotechnological applications crucial for human survival in the 21st century.

Tracking de novo protein synthesis in the activated sludge microbiome using BONCAT-FACS

In order to ensure stable performance of engineered biotechnologies that rely on mixed microbial community systems, it is important to identify process-specific microbial traits and study their in-situ activity and responses to changing environmental conditions and system operational parameters. We used BioOrthogonal Non-Canonical Amino acid Tagging (BONCAT) in combination with Fluorescence-Activated Cell Sorting (FACS) and 16S rRNA gene amplicon sequencing to identify translationally active cells in activated sludge. We found that only a subset of the activated sludge microbiome is translationally active during the aerobic treatment phase of a full-scale sequencing batch reactor designed to enhance biological phosphorus removal from municipal wastewater. Relative abundance of amplicon sequence variants was not a reliable predictor of species activity. BONCAT-positive and -negative cells revealed a broad range of population-wide and taxa-specific translational heterogeneity. BONCAT-FACS in combination with amplicon sequencing can provide new insights into the ecophysiology of highly dynamic microbiomes in activated sludge systems.

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.

Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model

MOTIVATION

Accumulating evidence has highlighted the importance of microbial interaction networks. Methods have been developed for estimating microbial interaction networks, of which the generalized Lotka-Volterra equation (gLVE)-based method can estimate a directed interaction network. The previous gLVE-based method for estimating microbial interaction networks did not consider time-varying interactions.

RESULTS

In this study, we developed unsupervised learning-based microbial interaction inference method using Bayesian estimation (Umibato), a method for estimating time-varying microbial interactions. The Umibato algorithm comprises Gaussian process regression (GPR) and a new Bayesian probabilistic model, the continuous-time regression hidden Markov model (CTRHMM). Growth rates are estimated by GPR, and interaction networks are estimated by CTRHMM. CTRHMM can estimate time-varying interaction networks using interaction states, which are defined as hidden variables. Umibato outperformed the existing methods on synthetic datasets. In addition, it yielded reasonable estimations in experiments on a mouse gut microbiota dataset, thus providing novel insights into the relationship between consumed diets and the gut microbiota.

AVAILABILITY AND IMPLEMENTATION

The C++ and python source codes of the Umibato software are available at https://github.com/shion-h/Umibato.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Axenic and Gnotobiotic Mosquito: Emerging Models for Microbiome Host Interactions

The increasing availability of modern research tools has enabled a revolution in studies of non-model organisms. Yet, one aspect that remains difficult or impossible to control in many model and most non-model organisms is the presence and composition of the host-associated microbiota or the microbiome. In this review, we explore the development of axenic (microbe-free) mosquito models and what these systems reveal about the role of the microbiome in mosquito biology. Additionally, the axenic host is a blank template on which a microbiome of known composition can be introduced, also known as a gnotobiotic organism. Finally, we identify a "most wanted" list of common mosquito microbiome members that show the greatest potential to influence host phenotypes. We propose that these are high-value targets to be employed in future gnotobiotic studies. The use of axenic and gnotobiotic organisms will transition the microbiome into another experimental variable that can be manipulated and controlled. Through these efforts, the mosquito will be a true model for examining host microbiome interactions.

Connecting structure and function from organisms to molecules in small-animal symbioses through chemo-histo-tomography

Metabolites mediate the establishment and persistence of most interkingdom symbioses. Still, to pinpoint the metabolites each partner displays upon interaction remains the biggest challenge in studying multiorganismal assemblages. Addressing this challenge, we developed a correlative imaging workflow to connect the in situ production of metabolites with the organ-scale and cellular three-dimensional distributions of mutualistic and pathogenic (micro)organisms in the same host animal. Combining mass spectrometry imaging and micro-computed X-ray tomography provided a culture-independent approach, which is essential to include the full spectrum of naturally occurring interactions. To introduce the potential of combining high-resolution tomography with metabolite imaging, we resolved the metabolic interactions between an invertebrate host, its symbiotic bacteria, and tissue parasites at unprecedented detail for model and nonmodel symbioses.

Our understanding of metabolic interactions between small symbiotic animals and bacteria or parasitic eukaryotes that reside within their bodies is extremely limited. This gap in knowledge originates from a methodological challenge, namely to connect histological changes in host tissues induced by beneficial and parasitic (micro)organisms to the underlying metabolites. We addressed this challenge and developed chemo-histo-tomography (CHEMHIST), a culture-independent approach to connect anatomic structure and metabolic function in millimeter-sized symbiotic animals. CHEMHIST combines chemical imaging of metabolites based on mass spectrometry imaging (MSI) and microanatomy-based micro-computed X-ray tomography (micro-CT) on the same animal. Both high-resolution MSI and micro-CT allowed us to correlate the distribution of metabolites to the same animal’s three-dimensional (3D) histology down to submicrometer resolutions. Our protocol is compatible with tissue-specific DNA sequencing and fluorescence in situ hybridization for the taxonomic identification and localization of the associated micro(organisms). Building CHEMHIST upon in situ imaging, we sampled an earthworm from its natural habitat and created an interactive 3D model of its physical and chemical interactions with bacteria and parasitic nematodes in its tissues. Combining MSI and micro-CT, we present a methodological groundwork for connecting metabolic and anatomic phenotypes of small symbiotic animals that often represent keystone species for ecosystem functioning.

Metabolic Profiling of Rhizobacteria Serratia plymuthica and Bacillus subtilis Revealed Intra- and Interspecific Differences and Elicitation of Plipastatins and Short Peptides Due to Co-cultivation

Rhizobacteria live in diverse and dynamic communities having a high impact on plant growth and development. Due to the complexity of the microbial communities and the difficult accessibility of the rhizosphere, investigations of interactive processes within this bacterial network are challenging. In order to better understand causal relationships between individual members of the microbial community of plants, we started to investigate the inter- and intraspecific interaction potential of three rhizobacteria, the S. plymuthica isolates 4Rx13 and AS9 and B. subtilis B2g, using high resolution mass spectrometry based metabolic profiling of structured, low-diversity model communities. We found that by metabolic profiling we are able to detect metabolite changes during cultivation of all three isolates. The metabolic profile of S. plymuthica 4Rx13 differs interspecifically to B. subtilis B2g and surprisingly intraspecifically to S. plymuthica AS9. Thereby, the release of different secondary metabolites represents one contributing factor of inter- and intraspecific variations in metabolite profiles. Interspecific co-cultivation of S. plymuthica 4Rx13 and B. subtilis B2g showed consistently distinct metabolic profiles compared to mono-cultivated species. Thereby, putative known and new variants of the plipastatin family are increased in the co-cultivation of S. plymuthica 4Rx13 and B. subtilis B2g. Interestingly, intraspecific co-cultivation of S. plymuthica 4Rx13 and S. plymuthica AS9 revealed a distinct interaction zone and showed distinct metabolic profiles compared to mono-cultures. Thereby, several putative short proline-containing peptides are increased in co-cultivation of S. plymuthica 4Rx13 with S. plymuthica AS9 compared to mono-cultivated strains. Our results demonstrate that the release of metabolites by rhizobacteria alters due to growth and induced by social interactions between single members of the microbial community. These results form a basis to elucidate the functional role of such interaction-triggered compounds in establishment and maintenance of microbial communities and can be applied under natural and more realistic conditions, since rhizobacteria also interact with the plant itself and many other members of plant and soil microbiota.

MetFish: a Metabolomics Pipeline for Studying Microbial Communities in Chemically Extreme Environments

Metabolites have essential roles in microbial communities, including as mediators of nutrient and energy exchange, cell-to-cell communication, and antibiosis. However, detecting and quantifying metabolites and other chemicals in samples having extremes in salt or mineral content using liquid chromatography-mass spectrometry (LC-MS)-based methods remains a significant challenge. Here, we report a facile method based on in situ chemical derivatization followed by extraction for analysis of metabolites and other chemicals in hypersaline samples, enabling for the first time direct LC-MS-based exometabolomics analysis in sample matrices containing up to 2 M total dissolved salts. The method, MetFish, is applicable to molecules containing amine, carboxylic acid, carbonyl, or hydroxyl functional groups, and it can be integrated into either targeted or untargeted analysis pipelines. In targeted analyses, MetFish provided limits of quantification as low as 1 nM, broad linear dynamic ranges (up to 5 to 6 orders of magnitude) with excellent linearity, and low median interday reproducibility (e.g., 2.6%). MetFish was successfully applied in targeted and untargeted exometabolomics analyses of microbial consortia, quantifying amino acid dynamics in the exometabolome during community succession; in situ in a native prairie soil, whose exometabolome was isolated using a hypersaline extraction; and in input and produced fluids from a hydraulically fractured well, identifying dramatic changes in the exometabolome over time in the well.

IMPORTANCE The identification and accurate quantification of metabolites using electrospray ionization-mass spectrometry (ESI-MS) in hypersaline samples is a challenge due to matrix effects. Clean-up and desalting strategies that typically work well for samples with lower salt concentrations are often ineffective in hypersaline samples. To address this gap, we developed and demonstrated a simple yet sensitive and accurate method—MetFish—using chemical derivatization to enable mass spectrometry-based metabolomics in a variety of hypersaline samples from varied ecosystems and containing up to 2 M dissolved salts.

Combining SIMS and mechanistic modelling to reveal nutrient kinetics in an algal-bacterial mutualism

Microbial communities are of considerable significance for biogeochemical processes, for the health of both animals and plants, and for biotechnological purposes. A key feature of microbial interactions is the exchange of nutrients between cells. Isotope labelling followed by analysis with secondary ion mass spectrometry (SIMS) can identify nutrient fluxes and heterogeneity of substrate utilisation on a single cell level. Here we present a novel approach that combines SIMS experiments with mechanistic modelling to reveal otherwise inaccessible nutrient kinetics. The method is applied to study the onset of a synthetic mutualistic partnership between a vitamin B₁₂-dependent mutant of the alga Chlamydomonas reinhardtii and the B₁₂-producing, heterotrophic bacterium Mesorhizobium japonicum, which is supported by algal photosynthesis. Results suggest that an initial pool of fixed carbon delays the onset of mutualistic cross-feeding; significantly, our approach allows the first quantification of this expected delay. Our method is widely applicable to other microbial systems, and will contribute to furthering a mechanistic understanding of microbial interactions.

Selfishness driving reductive evolution shapes interdependent patterns in spatially structured microbial communities

Microbes release a wide variety of metabolites to the environment that benefit the whole population, called public goods. Public goods sharing drives adaptive function loss, and allows the rise of metabolic cross-feeding. However, how public goods sharing governs the succession of communities over evolutionary time scales remains unclear. To resolve this issue, we constructed an individual-based model, where an autonomous population that possessed functions to produce three essential public goods, was allowed to randomly lose functions. Simulations revealed that function loss genotypes could evolve from the autonomous ancestor, driven by the selfish public production trade-off at the individual level. These genotypes could then automatically develop to three possible types of interdependent patterns: complete functional division, one-way dependency, and asymmetric functional complementation, which were influenced by function cost and function redundancy. In addition, we found random evolutionary events, i.e., the priority and the relative spatial positioning of genotype emergence, are also important in governing community assembly. Moreover, communities occupied by interdependent patterns exhibited better resistance to environmental perturbation, suggesting such patterns are selectively favored. Our work integrates ecological interactions with evolution dynamics, providing a new perspective to explain how reductive evolution shapes microbial interdependencies and governs the succession of communities.

gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism's genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present gapseq ( https://github.com/jotech/gapseq ), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.

Microbiologically-Synthesized Nanoparticles and Their Role in Silencing the Biofilm Signaling Cascade

The emergence of bacterial resistance to antibiotics has led to the search for alternate antimicrobial treatment strategies. Engineered nanoparticles (NPs) for efficient penetration into a living system have become more common in the world of health and hygiene. The use of microbial enzymes/proteins as a potential reducing agent for synthesizing NPs has increased rapidly in comparison to physical and chemical methods. It is a fast, environmentally safe, and cost-effective approach. Among the biogenic sources, fungi and bacteria are preferred not only for their ability to produce a higher titer of reductase enzyme to convert the ionic forms into their nano forms, but also for their convenience in cultivating and regulating the size and morphology of the synthesized NPs, which can effectively reduce the cost for large-scale manufacturing. Effective penetration through exopolysaccharides of a biofilm matrix enables the NPs to inhibit the bacterial growth. Biofilm is the consortia of sessile groups of microbial cells that are able to adhere to biotic and abiotic surfaces with the help extracellular polymeric substances and glycocalyx. These biofilms cause various chronic diseases and lead to biofouling on medical devices and implants. The NPs penetrate the biofilm and affect the quorum-sensing gene cascades and thereby hamper the cell-to-cell communication mechanism, which inhibits biofilm synthesis. This review focuses on the microbial nano-techniques that were used to produce various metallic and non-metallic nanoparticles and their "signal jamming effects" to inhibit biofilm formation. Detailed analysis and discussion is given to their interactions with various types of signal molecules and the genes responsible for the development of biofilm.

Lipopeptide-mediated bacterial interaction enables cooperative predator defense

Bacteria are inherently social organisms whose actions should ideally be studied within an interactive ecological context. We show that the exchange and modification of natural products enables two unrelated bacteria to defend themselves against a common predator. Amoebal predation is a major cause of death in soil bacteria and thus it exerts a strong selective pressure to evolve defensive strategies. A systematic analysis of binary combinations of coisolated bacteria revealed strains that were individually susceptible to predation but together killed their predator. This cooperative defense relies on a Pseudomonas species producing syringafactin, a lipopeptide, which induces the production of peptidases in a Paenibacillus strain. These peptidases then degrade the innocuous syringafactin into compounds, which kill the predator. A combination of bioprospecting, coculture experiments, genome modification, and transcriptomics unravel this novel natural product-based defense strategy.

Extracellular heme recycling and sharing across species by novel mycomembrane vesicles of a Gram-positive bacterium

Microbes spontaneously release membrane vesicles (MVs), which play roles in nutrient acquisition and microbial interactions. Iron is indispensable for microbes, but is a difficult nutrient to acquire. However, whether MVs are also responsible for efficient iron uptake and therefore involved in microbial interaction remains to be elucidated. Here, we used a Gram-positive strain, Dietzia sp. DQ12-45-1b, to analyze the function of its MVs in heme-iron recycling and sharing between species. We determined the structure and constituent of MVs and showed that DQ12-45-1b releases MVs originating from the mycomembrane. When comparing proteomes of MVs between iron-limiting and iron-rich conditions, we found that under iron-limiting conditions, heme-binding proteins are enriched. Next, we proved that MVs participate in extracellular heme capture and transport, especially in heme recycling from environmental hemoproteins. Finally, we found that the heme carried in MVs is utilized by multiple species, and we further verified that membrane fusion efficiency and species evolutionary distance determine heme delivery. Together, our findings strongly suggest that MVs act as a newly identified pathway for heme recycling, and represent a public good shared between phylogenetically closely related species.

Mass shift in mass spectrometry imaging: comprehensive analysis and practical corrective workflow

MALDI mass spectrometry imaging (MSI) allows the mapping and the tentative identification of compounds based on their m/z value. In typical MSI, a spectrum is taken at incremental 2D coordinates (pixels) across a sample surface. Single pixel mass spectra show the resolving power of the mass analyzer. Mass shift, i.e., variations of the m/z of the same ion(s), may occur from one pixel to another. The superposition of shifted masses from individual pixels peaks apparently degrades the resolution and the mass accuracy in the average spectrum. This leads to low confidence annotations and biased localization in the image. Besides the intrinsic performances of the analyzer, the sample properties (local composition, thickness, matrix deposition) and the calibration method are sources of mass shift. Here, we report a critical analysis and recommendations to mitigate these sources of mass shift. Mass shift 2D distributions were mapped to illustrate its effect and explore systematically its origin. Adapting the sample preparation, carefully selecting the data acquisition settings, and wisely applying post-processing methods (i.e., m/z realignment or individual m/z recalibration pixel by pixel) are key factors to lower the mass shift and to improve image quality and annotations. A recommended workflow, resulting from a comprehensive analysis, was successfully applied to several complex samples acquired on both MALDI ToF and MALDI FT-ICR instruments.

Metabolic Exchange and Energetic Coupling between Nutritionally Stressed Bacterial Species: Role of Quorum-Sensing Molecules

Formation of multispecies communities allows nearly every niche on earth to be colonized, and the exchange of molecular information among neighboring bacteria in such communities is key for bacterial success. To clarify the principles controlling interspecies interactions, we previously developed a coculture model with two anaerobic bacteria, Clostridium acetobutylicum (Gram positive) and Desulfovibrio vulgaris Hildenborough (Gram negative, sulfate reducing). Under conditions of nutritional stress for D. vulgaris, the existence of tight cell-cell interactions between the two bacteria induced emergent properties. Here, we show that the direct exchange of carbon metabolites produced by C. acetobutylicum allows D vulgaris to duplicate its DNA and to be energetically viable even without its substrates. We identify the molecular basis of the physical interactions and how autoinducer-2 (AI-2) molecules control the interactions and metabolite exchanges between C. acetobutylicum and D. vulgaris (or Escherichia coli and D. vulgaris). With nutrients, D. vulgaris produces a small molecule that inhibits in vitro the AI-2 activity and could act as an antagonist in vivo Sensing of AI-2 by D. vulgaris could induce formation of an intercellular structure that allows directly or indirectly metabolic exchange and energetic coupling between the two bacteria.IMPORTANCE Bacteria have usually been studied in single culture in rich media or under specific starvation conditions. However, in nature they coexist with other microorganisms and build an advanced society. The molecular bases of the interactions controlling this society are poorly understood. Use of a synthetic consortium and reducing complexity allow us to shed light on the bacterial communication at the molecular level. This study presents evidence that quorum-sensing molecule AI-2 allows physical and metabolic interactions in the synthetic consortium and provides new insights into the link between metabolism and bacterial communication.

Metagenomic assessment of the global diversity and distribution of bacteria and fungi

Bacteria and fungi are of uttermost importance in determining environmental and host functioning. Despite close interactions between animals, plants, their associated microbiomes, and the environment they inhabit, the distribution and role of bacteria and especially fungi across host and environments as well as the cross-habitat determinants of their community compositions remain little investigated. Using a uniquely broad global dataset of 13 483 metagenomes, we analysed the microbiome structure and function of 25 host-associated and environmental habitats, focusing on potential interactions between bacteria and fungi. We found that the metagenomic relative abundance ratio of bacteria-to-fungi is a distinctive microbial feature of habitats. Compared with fungi, the cross-habitat distribution pattern of bacteria was more strongly driven by habitat type. Fungal diversity was depleted in host-associated communities compared with those in the environment, particularly terrestrial habitats, whereas this diversity pattern was less pronounced for bacteria. The relative gene functional potential of bacteria or fungi reflected their diversity patterns and appeared to depend on a balance between substrate availability and biotic interactions. Alongside helping to identify hotspots and sources of microbial diversity, our study provides support for differences in assembly patterns and processes between bacterial and fungal communities across different habitats.

Exometabolite Dynamics over Stationary Phase Reveal Strain-Specific Responses

Microbial exponential growth is expected to occur infrequently in environments that have long periods of nutrient starvation punctuated by short periods of high nutrient flux. These conditions likely impose nongrowth states for microbes. However, nongrowth states are uncharacterized for the majority of environmental bacteria, especially in regard to exometabolite production. We compared exometabolites produced over stationary phase across three environmental bacteria: Burkholderia thailandensis E264 (ATCC 700388), Chromobacterium violaceum ATCC 31532, and Pseudomonas syringae pv. tomato DC3000 (ATCC BAA-871). We grew each strain in monoculture and investigated exometabolite dynamics from mid-exponential to stationary phases. We focused on exometabolites that were released into the medium and accumulated over 45 h, including approximately 20 h of stationary phase. We also analyzed transcripts (transcriptome sequencing [RNA-seq]) to interpret exometabolite output. We found that the majority of exometabolites released were strain specific, with a subset of identified exometabolites involved in both central and secondary metabolism. Transcript analysis supported that exometabolites were released from intact cells, as various transporters had either increased or consistent transcripts through time. Interestingly, we found that succinate was one of the most abundant identifiable exometabolites for all strains and that each strain rerouted their metabolic pathways involved in succinate production during stationary phase. These results show that nongrowth states can be metabolically dynamic and that environmental bacteria can enrich a minimal environment with diverse chemical compounds as a consequence of growth and postgrowth maintenance in stationary phase. This work provides insights into microbial community interactions via exometabolites under conditions of growth cessation or limitation.IMPORTANCE Nongrowth states are common for bacteria that live in environments that are densely populated and predominantly nutrient exhausted, and yet these states remain largely uncharacterized in cellular metabolism and metabolite output. Here, we investigated and compared stationary-phase exometabolites and RNA transcripts for each of three environmental bacterial strains. We observed that diverse exometabolites were produced and provide evidence that these exometabolites accumulate over time through release by intact cells. Additionally, each bacterial strain had a characteristic exometabolite profile and exhibited dynamics in exometabolite composition. This work affirms that stationary phase is metabolically dynamic, with each strain tested creating a unique chemical signature in the extracellular space and altering metabolism in stationary phase. These findings set the stage for understanding how bacterial populations can support surrounding neighbors in environments with prolonged nutrient exhaustion through exometabolite-mediated interspecies interactions.

Expansin-related proteins: biology, microbe-plant interactions and associated plant-defense responses

Expansins, cerato-platanins and swollenins (which we will henceforth refer to as expansin-related proteins) are a group of microbial proteins involved in microbe-plant interactions. Although they share very low sequence similarity, some of their composing domains are near-identical at the structural level. Expansin-related proteins have their target in the plant cell wall, in which they act through a non-enzymatic, but still uncharacterized, mechanism. In most cases, mutagenesis of expansin-related genes affects plant colonization or plant pathogenesis of different bacterial and fungal species, and thus, in many cases they are considered virulence factors. Additionally, plant treatment with expansin-related proteins activate several plant defenses resulting in the priming and protection towards subsequent pathogen encounters. Plant-defence responses induced by these proteins are reminiscent of pattern-triggered immunity or hypersensitive response in some cases. Plant immunity to expansin-related proteins could be caused by the following: (i) protein detection by specific host-cell receptors, (ii) alterations to the cell-wall-barrier properties sensed by the host, (iii) displacement of cell-wall polysaccharides detected by the host. Expansin-related proteins may also target polysaccharides on the wall of the microbes that produced them under certain physiological instances. Here, we review biochemical, evolutionary and biological aspects of these relatively understudied proteins and different immune responses they induce in plant hosts.

Regularized S-Map Reveals Varying Bacterial Interactions

There is a growing awareness that bacterial interactions follow a highly nonlinear pattern in reality. However, it is challenging to track the varying bacterial interactions using pairwise correlation analysis, which fails to explore their potential effects on the behavior of microbes. Here, we utilized a regularized sequential locally weighted global linear map (S-map) to capture the varying interspecific interactions from the time series data of a bacterial community under exposure to nitrite. Our results show that bacterial interactions are highly variable and that asymmetric interactions dominate the interaction pattern in a community. Furthermore, we propose a Jacobian coefficient-based statistical method to predict the harmony level of a bacterial community at each successive ecosystem state. The results show that the bacterial community exhibits a higher harmony level in nitrite-treated samples than in the control group. We show that the community harmony level is positively associated with the specific endogenous respiration rates and biofilm formation of the culture. In addition, the community tends to process lower diversity and structural stability under zero- and high-nitrite stresses. We demonstrate that the harmony level, rather than structural stability, is a useful index for unveiling the underlying mechanism of bacterial performance. Overall, the regularized S-map can help us to understand bacterial interactions in ecosystems more accurately than previous approaches.IMPORTANCE It has long been acknowledged that bacterial interactions play important roles in community structure and function. Revealing the interaction variability can allow an understanding of how bacteria respond to perturbation and why bacterial community performance changes. Such information should improve our skills in engineering bacterial communities (e.g., in a wastewater treatment plant) and achieve better removal performance and lower energy consumption.

A guide to deciphering microbial interactions and metabolic fluxes in microbiome communities

Microbiomes occupy nearly all environments on Earth. These communities of interacting microorganisms are highly complex, dynamic biological systems that impact and reshape the molecular composition of their habitats by performing complex biochemical transformations. The structure and function of microbiomes are influenced by local environmental stimuli and spatiotemporal changes. In order to control the dynamics and ultimately the function of microbiomes, we need to develop a mechanistic and quantitative understanding of the ecological, molecular, and evolutionary driving forces that govern these systems. Here, we describe recent advances in developing computational and experimental approaches that can promote a more fundamental understanding of microbial communities through comprehensive model-based analysis of heterogeneous data types across multiple scales, from intracellular metabolism, to metabolite cross-feeding interactions, to the emergent macroscopic behaviors. Ultimately, harnessing the full potential of microbiomes for practical applications will require developing new predictive modeling approaches and better tools to manipulate microbiome interactions.

Mass Spectrometry: A Rosetta Stone to Learn How Fungi Interact and Talk

Fungi are a highly diverse group of heterotrophic organisms that play an important role in diverse ecological interactions, many of which are chemically mediated. Fungi have a very versatile metabolism, which allows them to synthesize a large number of still little-known chemical compounds, such as soluble compounds that are secreted into the medium and volatile compounds that are chemical mediators over short and long distances. Mass spectrometry (MS) is currently playing a dominant role in mycological studies, mainly due to its inherent sensitivity and rapid identification capabilities of different metabolites. Furthermore, MS has also been used as a reliable and accurate tool for fungi identification (i.e., biotyping). Here, we introduce the readers about fungal specialized metabolites, their role in ecological interactions and provide an overview on the MS-based techniques used in fungal studies. We particularly present the importance of sampling techniques, strategies to reduce false-positive identification and new MS-based analytical strategies that can be used in mycological studies, further expanding the use of MS in broader applications. Therefore, we foresee a bright future for mass spectrometry-based research in the field of mycology.

Biokinetics of protein degrading Clostridium cadaveris and Clostridium sporogenes in batch and continuous mode of operations

A quantitative real-time polymerase chain reaction (QPCR) was applied to estimate biokinetic coefficients of Clostridium cadaveris and Clostridium sporogenes, which utilize protein as carbon source. Experimental data of changes in peptone concentration and 16S rRNA gene copy numbers of C. cadaveris and C. sporogenes were fitted to model. The fourth-order Runge-Kutta approximation with non-linear least squared analysis was employed to solve the ordinary differential equations to estimate biokinetic coefficients. The maximum specific growth rate (μ_max), half saturation concentration (K_s), growth yield (Y), and decay coefficient (K_d) of C. cadaveris and C.sporogenes were 0.73 ± 0.05 and 1.35 ± 0.32 h^-1, 6.07 ± 1.52 and 5.67 ± 1.53 g/L, 2.25 ± 0.75 × 10¹⁰ and 7.92 ± 3.71 × 10⁹ copies/g, 0.002 ± 0.003 and 0.002 ± 0.001 h^-1, respectively. The theoretical specific growth rate of C. sporogenes always exceeded than that of C. cadaveris at peptone concentration higher than 3.62 g/L. When the influent peptone concentration was 5.0 g/L, the concentration of C.cadaveris gradually decreased to the steady value of 2.9 × 10¹⁰ copies/mL at 4 hours HRT, which indicates 67.1% of the initial population reduction, but the wash out occurred at 1.9 and 3.2 hours HRTs. The 16S rRNA gene copy numbers of C. sporogenes gradually decreased to steady values ranging from 1.1 × 10¹⁰ to 2.9 × 10¹⁰ copies/mL. C. sporogenes species was predicted to wash out at an HRT of 1.6 h.

Genetically engineered control of phenotypic structure in microbial colonies

Rapid advances in cellular engineering^1,2 have positioned synthetic biology to address therapeutic^3,4 and industrial⁵ problems, but a substantial obstacle is the myriad of unanticipated cellular responses in heterogeneous real-world environments such as the gut^6,7, solid tumours^8,9, bioreactors¹⁰ or soil¹¹. Complex interactions between the environment and cells often arise through non-uniform nutrient availability, which generates bidirectional coupling as cells both adjust to and modify their local environment through phenotypic differentiation^12,13. Although synthetic spatial gene expression patterns^14-17 have been explored under homogeneous conditions, the mutual interaction of gene circuits, growth phenotype and the environment remains a challenge. Here, we design gene circuits that sense and control phenotypic structure in microcolonies containing both growing and dormant bacteria. We implement structure modulation by coupling different downstream modules to a tunable sensor that leverages Escherichia coli's stress response and is activated on growth arrest. One is an actuator module that slows growth and thereby alters nutrient gradients. Environmental feedback in this circuit generates robust cycling between growth and dormancy in the interior of the colony, as predicted by a spatiotemporal computational model. We also use the sensor to drive an inducible gating module for selective gene expression in non-dividing cells, which allows us to radically alter population structure by eliminating the dormant phenotype with a 'stress-gated lysis circuit'. Our results establish a strategy to leverage and control microbial colony structure for synthetic biology applications in complex environments.

Diversity and Distribution of Volatile Secondary Metabolites Throughout Bacillus subtilis Isolates

Bacillus subtilis releases a broad range of volatile secondary metabolites, which are considered as long- and short distance infochemical signals mediating inter- and intra-specific processes. In addition, they often show antimicrobial or antifungal activities. This review attempts to summarize yet known volatile secondary metabolites produced and emitted by Bacillus subtilis isolates focusing on the structural diversity and distribution patterns. Using in vitro volatile-collection systems, 26 strains of B. subtilis isolated from different habitats were found to produce in total 231 volatile secondary metabolites. These volatile secondary metabolites comprised mainly hydrocarbons, ketones, alcohols, aldehydes, ester, acids, aromatics, sulfur- and nitrogen-containing compounds. Reviewed data revealed to a great extent isolate-specific emission patterns. The production and release of several volatile bioactive compounds was retained in isolates of the species B. subtilis, while volatiles without a described function seemed to be isolate-specifically produced. Detailed analysis, however, also indicated that the original data were strongly influenced by insufficient descriptions of the bacterial isolates, heterogeneous and poorly documented culture conditions as well as sampling techniques and inadequate compound identification. In order to get deeper insight into the nature, diversity, and ecological function of volatile secondary metabolites produced by B. subtilis, it will be necessary to follow well-documented workflows and fulfill state-of-the-art standards to unambiguously identify the volatile metabolites. Future research should consider the dynamic of a bacterial culture leading to differences in cell morphology and cell development. Single cell investigations could help to attribute certain volatile metabolites to defined cell forms and developmental stages.

Biotransformation of 5-Hydroxymethylfurfural to 2,5-Furandicarboxylic Acid by a Syntrophic Consortium of Engineered Synechococcus elongatus and Pseudomonas putida

2,5-furandicarboxylic acid (FDCA) is one of the top platform chemicals that can be produced from biomass feedstock. To make the cost of industrial FDCA production compatible with plastics made from fossils, the price of substrates and process complexity should be reduced. The aim of this research is to create a CO₂ -driven syntrophic consortium for the catalytic conversion of renewable biomass-derived 5-hydroxymethylfurfural (HMF) to FDCA. Sucrose produced from carbon fixation by the engineered Synechococcus elongatus serves as the sole carbon source for the engineered Pseudomonas putida to catalyze the reaction of HMF to FDCA. The yield of FDCA by the consortium reaches around 70% while the conversion of HMF is close to 100%. With further surface engineering to clump the two strains, the FDCA yield is elevated to almost 100% via the specific association between an Src homology 3 (SH3) domain and its ligand. The syntrophic consortium successfully demonstrates its green and cost-effective characteristics for the conversion of CO₂ and biomass into platform chemicals.

Spatial metabolomics of in situ host-microbe interactions at the micrometre scale

Spatial metabolomics describes the location and chemistry of small molecules involved in metabolic phenotypes, defence molecules and chemical interactions in natural communities. Most current techniques are unable to spatially link the genotype and metabolic phenotype of microorganisms in situ at a scale relevant to microbial interactions. Here, we present a spatial metabolomics pipeline (metaFISH) that combines fluorescence in situ hybridization (FISH) microscopy and high-resolution atmospheric-pressure matrix-assisted laser desorption/ionization mass spectrometry to image host-microbe symbioses and their metabolic interactions. The metaFISH pipeline aligns and integrates metabolite and fluorescent images at the micrometre scale to provide a spatial assignment of host and symbiont metabolites on the same tissue section. To illustrate the advantages of metaFISH, we mapped the spatial metabolome of a deep-sea mussel and its intracellular symbiotic bacteria at the scale of individual epithelial host cells. Our analytical pipeline revealed metabolic adaptations of the epithelial cells to the intracellular symbionts and variation in metabolic phenotypes within a single symbiont 16S rRNA phylotype, and enabled the discovery of specialized metabolites from the host-microbe interface. metaFISH provides a culture-independent approach to link metabolic phenotypes to community members in situ and is a powerful tool for microbiologists across fields.

Design of Fungal Co-Cultivation Based on Comparative Metabolomics and Bioactivity for Discovery of Marine Fungal Agrochemicals

Microbial co-cultivation is employed for awakening silent biosynthetic gene clusters (BGCs) to enhance chemical diversity. However, the selection of appropriate partners for co-cultivation remains a challenge. Furthermore, competitive interactions involving the suppression of BGCs or upregulation of known, functional metabolite(s) during co-cultivation efforts is also common. Herein, we performed an alternative approach for targeted selection of the best co-cultivation pair. Eight marine sediment-derived fungi were classified as strong or weak, based on their anti-phytopathogenic potency. The fungi were co-cultured systematically and analyzed for their chemical profiles and anti-phytopathogenic activity. Based on enhanced bioactivity and a significantly different metabolite profile including the appearance of a co-culture specific cluster, the co-culture of Plenodomus influorescens (strong) and Pyrenochaeta nobilis (weak) was prioritized for chemical investigation. Large-scale co-cultivation resulted in isolation of five polyketide type compounds: two 12-membered macrolides, dendrodolide E (1) and its new analog dendrodolide N (2), as well as two rare azaphilones spiciferinone (3) and its new analog 8a-hydroxy-spiciferinone (4). A well-known bis-naphtho-γ-pyrone type mycotoxin, cephalochromin (5), whose production was specifically enhanced in the co-culture, was also isolated. Chemical structures of compounds 1-5 were elucidated by NMR, HRMS and [] analyses. Compound 5 showed the strongest anti-phytopathogenic activity against Xanthomonas campestris and Phytophthora infestans with IC₅₀ values of 0.9 and 1.7 µg/mL, respectively.

Structure, properties, and biological functions of nonribosomal lipopeptides from pseudomonads

Bacteria of the genusPseudomonasdisplay a fascinating metabolic diversity. In this review, we focus our attention on the natural product class of nonribosomal lipopeptides, which help pseudomonads to colonize a wide range of ecological niches.

Feature-Based Molecular Networking for Metabolite Annotation

The Global Natural Product Social Molecular Networking (GNPS) platform leverages tandem mass spectrometry (MS/MS) data for annotation of compounds. Molecular networks aid in the visualization of the chemical space within a metabolomics experiment. Recently, molecular networking has been combined with feature detection methods to yield Feature-Based Molecular Networking (FBMN). FBMN allows for the discrimination of isomers within the molecular network, incorporation of quantitative information generated by the feature detection tools into visualization of the molecular network, and compatibility with forthcoming in silico annotation tools. This chapter provides step-by-step methods for generating a molecular network to annotate microbial natural products using the Global Natural Product Social Molecular Networking (GNPS) Feature-Based Molecular Networking (FBMN) workflow.

Common principles and best practices for engineering microbiomes

Despite broad scientific interest in harnessing the power of Earth's microbiomes, knowledge gaps hinder their efficient use for addressing urgent societal and environmental challenges. We argue that structuring research and technology developments around a design-build-test-learn (DBTL) cycle will advance microbiome engineering and spur new discoveries of the basic scientific principles governing microbiome function. In this Review, we present key elements of an iterative DBTL cycle for microbiome engineering, focusing on generalizable approaches, including top-down and bottom-up design processes, synthetic and self-assembled construction methods, and emerging tools to analyse microbiome function. These approaches can be used to harness microbiomes for broad applications related to medicine, agriculture, energy and the environment. We also discuss key challenges and opportunities of each approach and synthesize them into best practice guidelines for engineering microbiomes. We anticipate that adoption of a DBTL framework will rapidly advance microbiome-based biotechnologies aimed at improving human and animal health, agriculture and enabling the bioeconomy.