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Cooperative anaerobic catabolism of chlorinated organic compounds: implications for sustainable bioremediation.

Biodegradation research historically followed a reductionist approach focused on axenic (pure) cultures capable of catabolizing the specific contaminant(s) of interest. While this approach has substantially advanced our understanding of the microbiology, physiology, biochemistry, and genetics of contaminant degradation under laboratory conditions, it does not capture the complexity of natural and engineered environments. During in situ bioremediation, microbiomes are exposed to mixtures of contaminants, and microbial interactions profoundly influence contaminant transformation and fate. In anoxic environments, degradation of chlorinated compounds is often sustained by metabolic cooperation among taxonomically and physiologically distinct microorganisms. Through the exchange of metabolites such as hydrogen, formate, acetate, and other nutrients, microbial populations establish interdependent networks that overcome thermodynamic and physiological constraints, enabling self-sustaining systems of contaminant transformations that would be inefficient or impossible with individual organisms. We highlight examples of microbial interactions that underpin anaerobic catabolism of chlorinated contaminants, including systems resulting in self-sustained anaerobic bioremediation.

Biodegradation, Environmental↗

Toward an understanding of microbial communities through analysis of communication networks.

Bacteria receive signals from diverse members of their biotic environment. They sense their own species through the process of quorum sensing, which detects the density of bacterial cells and regulates functions such as bioluminescence, virulence, and competence. Bacteria also respond to the presence of other microorganisms and eukaryotic hosts. Most studies of microbial communication focus on signaling between the microbe and one other organism for empirical simplicity and because few experimental systems offer the opportunity to study communication among various types of organisms. But in the real biological world, microorganisms must carry on multiple molecular conversations simultaneously between diverse organisms, thereby constructing communication networks. We propose that biocontrol of plant disease, the process of suppressing disease through application of a microorganism, offers a model for the study of communication among multiple organisms. Successful biocontrol requires the sending and receiving of signals between the biocontrol agent and the pathogen, plant host, and microbial community surrounding the host. We are using Bacillus cereus, a biocontrol agent, and the organisms it must interact with, to dissect a communication network. This system offers an excellent starting point for study because its members are defined and well studied. An understanding of signaling in the B. cereus biocontrol system may provide a model for network communication among organisms that share a habitat and provide a new angle of analysis for understanding the interconnections that define communities.

Bacillus cereus↗

Optical lectin based biosensor as tool for bacteria identification.

Biosensor techniques are based on biospecific interaction between the biological parts of biosensor with the analyte. In biosensor construction, antibodies are usually used for the detection of analytes such as microorganism, because of very strong and highly specific interaction. The disadvantages of this assay are a long time needed for antibody isolation and purification as well as difficult regeneration of biosensor chip. The use of lectins instead of antibodies could solve these problems because a several hundred lectins are commercially available and their stability in standard buffers is better compared to monoclonal antibodies. While antibody can only be used to detect that antigen it was designed for, lectin as low affinity molecule may bind several different pathogens. Using the discriminative effect of an artificial neural network the application of a lectin array will compensate for the lower specificity. Microbial surfaces bear many of the sugar residues capable of interacting with lectins. The ability of lectins to react with microbial glycoconjugates means that it is possible to employ them as probes and sorbents for whole cells, mutants and numerous cellular constituents and metabolites, and it makes them useful tools for identification or typing of bacteria. Lectins are attractive reagents for the clinical diagnostic laboratory because of their diverse specificity, commercial availability, a wide range of molecular weights, and their stability in standard buffers. The construction of lectin biosensor could be an advantage method for detection of pathogenic bacteria.

Animals↗

Cross-domain cooperation drives nutrient acquisition and metabolism in the bark beetle holobiont.

Microbial symbiosis underpins host adaptation, yet mechanisms of metabolic integration in holobionts remain unclear. Using metatranscriptomics, genomics, and metabolic assays, we investigated gut microbiome interactions in the European spruce bark beetle (Ips typographus). We observed metabolic complementarity among symbionts and host, forming cross-domain networks that support nutrient acquisition. Nitrogen recycling revealed strong interdependence: no single partner possessed a complete uric acid degradation pathway, but combined evidence supports a distributed pathway spanning beetle, Bacteria, and fungi. Additionally, bacterial nitrate reduction to ammonia indicates a potential nitrogen influx, making otherwise inaccessible inorganic nitrogen available to the host. Shaped by microbial interactions, symbionts also likely supply specific amino acids, while vitamin metabolism showed cross-domain co-metabolism, with Bacteria as main producers of B vitamins, while host and fungi modulated interconversion. Carbohydrate degradation was highly partitioned; bacteria target xylan and pectin, while fungi contribute to glucan breakdown. Crucially, our data provide indirect evidence that the beetle may contribute to complete cellulose degradation, highlighting an underappreciated host role in lignocellulose processing. In terms of enzymatic functional diversity, the bacteriome emerged as the most important microbiome component-an observation that contrasts with the traditional focus on fungi and underscores the need to consider bacterial contributions in insect symbioses. Despite life-stage variation, core metabolic functions remained stable. Overall, metabolic interdependence, rather than microbial composition alone, structures holobiont function. These results highlight functional redundancy and ecological resilience, emphasizing the importance of microbial cooperation and host-microbe metabolic evolution.

Bark beetle↗

A graph-based approach for the visualisation and analysis of bacterial pangenomes.

BACKGROUND: The advent of low cost, high throughput DNA sequencing has led to the availability of thousands of complete genome sequences for a wide variety of bacterial species. Examining and interpreting genetic variation on this scale represents a significant challenge to existing methods of data analysis and visualisation. RESULTS: Starting with the output of standard pangenome analysis tools, we describe the generation and analysis of interactive, 3D network graphs to explore the structure of bacterial populations, the distribution of genes across a population, and the syntenic order in which those genes occur, in the new open-source network analysis platform, Graphia. Both the analysis and the visualisation are scalable to datasets of thousands of genome sequences. CONCLUSIONS: We anticipate that the approaches presented here will be of great utility to the microbial research community, allowing faster, more intuitive, and flexible interaction with pangenome datasets, thereby enhancing interpretation of these complex data.

Bacteria↗

Host-pathogen systems biology.

Unlike traditional biological research that focuses on a small set of components, systems biology studies the complex interactions between a large number of genes, proteins and other elements of biological networks and systems. Host-pathogen systems biology examines the interactions between the components of two distinct organisms, either a microbial or viral pathogen and its animal host or two different microbial species in a community. With the availability of complete genomic sequences of various hosts and pathogens, together with breakthroughs in proteomics, metabolomics and other experimental areas, the investigation of host-pathogen systems on a multitude of levels of detail has come within reach.

Animals↗

Competition and cooperation: The plasticity of bacterial interactions across environments.

Bacteria live in diverse communities, forming complex networks of interacting species. A central question in bacterial ecology is whether species engage in cooperative or competitive interactions. But this question often neglects the role of the environment. Here, we use genome-scale metabolic networks from two different open-access collections (AGORA and CarveMe) to assess pairwise interactions of different microbes in varying environmental conditions (provision of different environmental compounds). By computationally simulating thousands of environments for 10,000 pairs of bacteria from each collection, we found that most pairs were able to both compete and cooperate depending on the availability of environmental resources. This modeling approach allowed us to determine commonalities between environments that could facilitate the potential for cooperation or competition between a pair of species. Namely, cooperative interactions, especially obligate, were most common in less diverse environments. Further, as compounds were removed from the environment, we found interactions tended to degrade towards obligacy. However, we also found that on average at least one compound could be removed from an environment to switch the interaction from competition to facultative cooperation or vice versa. Together our approach indicates a high degree of plasticity in microbial interactions in response to the availability of environmental resources.

Microbial Interactions↗

Plant-derived and microbial biostimulants in sustainable agriculture: mechanisms, applications, and challenges.

Plant biostimulants have emerged as transformative and sustainable tools for improving crop productivity, resource-use efficiency, and resilience under rapidly intensifying environmental stresses. Unlike conventional agrochemicals, biostimulants function by activating physiological, biochemical, and molecular processes that optimize plant performance without directly supplying nutrients or exerting pesticidal effects. This review comprehensively examines the integrated roles of plant-derived and microbial biostimulants in sustainable agriculture, with particular emphasis on microbial-mediated mechanisms underlying plant stress adaptation and rhizosphere functioning. Plant-derived biostimulants, including seaweed extracts, humic substances, protein hydrolysates, amino acids, and chitosan, enhance nutrient acquisition, root architecture, hormonal regulation, and antioxidant defense systems. More importantly, microbial biostimulants, such as plant growth-promoting rhizobacteria (PGPR), endophytic microorganisms, mycorrhizal fungi, actinomycetes, yeasts, and cyanobacteria, exert multifunctional effects through biological nitrogen fixation, mineral solubilization, phytohormone biosynthesis, volatile signaling, osmolyte accumulation, pathogen suppression, and modulation of stress-responsive genes. These beneficial microorganisms reshape rhizosphere microbial communities, improve nutrient cycling, and enhance plant tolerance to drought, salinity, heat, and heavy metal toxicity. Emerging evidence from genomics, transcriptomics, metabolomics, and microbiome-based investigations has further revealed the molecular networks and signaling pathways governing biostimulant-induced resilience and plant-microbe interactions. Despite their substantial promise, inconsistent field performance, formulation instability, regulatory limitations, and inadequate mechanistic understanding continue to restrict their large-scale adoption. This review highlights recent advances in microbial and plant-derived biostimulants while identifying critical knowledge gaps and future opportunities for precision biostimulant engineering, microbiome manipulation, and climate-resilient crop management. The integration of next generation biostimulant technologies into sustainable agricultural systems may significantly reduce dependence on agrochemicals while improving crop productivity, environmental sustainability, and global food security.

Agriculture↗

Application of artificial neural networks to describe the combined effect of pH and NaCl on the heat resistance of Bacillus stearothermophilus.

A model for prediction of bacterial spore inactivation was developed. The influence of temperature, pH and NaCl on the heat resistance of Bacillus stearothermophilus spores was described using low-complexity, black box models based on artificial neural networks. Literature data were used to build and train the neural network, and new experimental data were used to evaluate it. The neural network models gave better predictions than the classical quadratic response surface model in all the experiments tried. When the neural networks were evaluated using new experimental data, also good predictions were obtained, providing fail-safe predictions of D values in all cases. The weights and biases values of neurons of the neural network that gave the best results are presented, so the reader can use the model for their own purposes. The use of this non-linear modelling technique makes it possible to describe more accurately interacting effects of environmental factors when compared with classical predictive microbial models.

Food Microbiology↗

In silico encounters: harnessing metabolic modelling to understand plant-microbe interactions.

Understanding plant-microbe interactions is vital for developing sustainable agricultural practices and mitigating the consequences of climate change on food security. Plant-microbe interactions can improve nutrient acquisition, reduce dependency on chemical fertilizers, affect plant health, growth, and yield, and impact plants' resistance to biotic and abiotic stresses. These interactions are largely driven by metabolic exchanges and can thus be understood through metabolic network modelling. Recent developments in genomics, metagenomics, phenotyping, and synthetic biology now enable researchers to harness the potential of metabolic modelling at the genome scale. Here, we review studies that utilize genome-scale metabolic modelling to study plant-microbe interactions in symbiotic, pathogenic, and microbial community systems. This review catalogues how metabolic modelling has advanced our understanding of the plant host and its associated microorganisms as a holobiont. We showcase how these models can contextualize heterogeneous datasets and serve as valuable tools to dissect and quantify underlying mechanisms. Finally, we consider studies that employ metabolic models as a testbed for in silico design of synthetic microbial communities with predefined traits. We conclude by discussing broader implications of the presented studies, future perspectives, and outstanding challenges.

Plants↗

The truth about antibiotics.

Microbes produce millions of organic compounds of low molecular weight--a world of very diverse chemical and biological ecology. We propose that, at the low concentrations likely to be found in the environment, the majority of these compounds play important roles in the modulation of metabolic function in natural microbial communities. The biological diversity is reflected by distinct target responses affecting a variety of transcription regulatory networks by different mechanisms. This provides the basis of chemical signalling processes in the microbial world and may well extend into many prokaryote-eukaryote interactions.

Anti-Bacterial Agents↗

SimpleMicrobiome: An integrated web-based platform for streamlined microbiome data analysis and visualization.

Microbiome studies require multiple analytical steps after initial sequence processing. These steps commonly include data harmonization, preprocessing, taxonomic profiling, diversity analysis, differential abundance testing, predictive modeling, network inference, and preparation of publication-ready outputs. Although robust packages are available for many of these tasks, routine use often depends on command-line workflows, repeated data reformatting, and method-specific scripting. These requirements can limit accessibility for experimental researchers and complicate consistent analysis across interdisciplinary teams. We developed SimpleMicrobiome, a web-based R Shiny platform that integrates established microbiome analysis methods into a single interactive downstream workflow. The application accepts standard abundance, taxonomy, and metadata tables, supports interactive preprocessing and sample filtering, and provides modules for taxa profile visualization, alpha and beta diversity analysis, ANCOM-BC2 and MaAsLin2 differential abundance testing, Random Forest modeling with SHAP-based interpretation, microbial association network inference using SparCC and SPIEC-EASI through NetCoMi, correlation heatmaps, and dbRDA/CAP-style association biplots. The platform is implemented as a modular Shiny application so that preprocessing choices are propagated across downstream analyses, results can be exported as figures and tables, and the same application can be run through the public server, source-code installation, or a Docker image. SimpleMicrobiome consolidates major downstream microbiome analysis tasks in an accessible browser-based environment while retaining links to established analytical frameworks. The platform may reduce technical barriers for non-programming users, improve consistency across exploratory and reporting-oriented analyses, and support collaborative microbiome research. The public application is available at https://simplemicrobiome.mglab.org, the source code is available at https://github.com/yjcho2252/SimpleMicrobiome, and a Docker image for local deployment is available at https://hub.docker.com/r/mglab2252/simplemicrobiome.

differential abundance↗

How to build an adapted and bioactive cell microenvironment? A chemical interaction study of the structure of Ca-alginate matrices and their repercussion on confined cells.

Alginates are increasingly being used as medical materials (matrices for tissue regeneration, surgical sponges, hemostatic bandages, microbial and cell encapsulation, artificial bacterial biofilms, etc.). The constitution of alginate gel networks is a complex phenomenon. A great number of different kinds of polysaccharidic frameworks can come to existence depending on the conditions used for their attainment. For instance, the degree of heterogeneity and porosity of Ca-alginate beads rely on this molecular organization. The formation of structural irregularities (superficial crust, cavities, shafts, dense or light gel frameworks, ordered or chaotic domains, etc.) within the alginate gel beads are inherent to this skeletal design. Several specific staining molecules (e.g. calcon carboxylic acid, murexide, methylene blue) that are negatively or positively charged interact with the gel network. These molecules allowed us to reveal a great variety of chemical interactions shown by the pattern coloration of the internal structure of the gel. The results observed are very different for the several matrices analyzed, which could explain to a great extent the singular behavior that cells confined in these kind of matrices exhibit.

Alginates↗

Influence of toll-like receptor 4, CD14, tumor necrosis factor, and interleukine-10 gene polymorphisms on clinical outcome in Japanese critically ill patients.

The innate recognition of microbial components and subsequent activation of cytokine network are important in the pathophysiology of sepsis. Recently, functional gene polymorphisms in molecules associated with these responses were demonstrated. On the other hand, it has been claimed that there are ethnic differences in genetic polymorphisms. This study investigated toll-like receptor (TLR) 4, CD14, tumor necrosis factor (TNF)-alpha and -beta, and interleukin (IL)-10 gene polymorphisms in 197 Japanese critically ill patients and 214 healthy control subjects to evaluate the influence of these polymorphisms on clinical outcome. No Japanese participant carrying TLR4Asp299Gly or Thr399Ile was detected. No association of CD14-159C/T polymorphisms with genotype frequency or sepsis mortality was observed. Frequency of TNF-alpha-308 GA genotype was significantly higher in the sepsis group than in the control group and TNF-alpha-308GA and IL-10-592CC genotypes were related to poor outcome of sepsis. Ethnic differences in genetic variations are very important issues and the frequencies in this study differ from those previously reported in Caucasians. In conclusion, this study may indicate that TNF-alpha-308G/A and IL-10-592C/A polymorphisms involved in subsequent activation of cytokine network had a larger effect on clinical outcome in patients with sepsis than TLR4Asp299Gly, Thr399Ile, and CD14-159C/T polymorphisms associated with the initial host-microbial interaction.

APACHE↗

Influence of mycotoxin producing fungi (Fusarium, Aspergillus, Penicillium) on gluten proteins during suboptimal storage of wheat after harvest and competitive interactions between field and storage fungi.

Cereals contaminated by Aspergillus spp., Penicillium spp., and Fusarium spp. and their mycotoxins, for example, ochratoxin A (OTA) and deoxynivalenol (DON), are not only a risk to human and animal health but can also show poor technological properties and baking quality. The influence of these genera on the sulfur speciation of low molecular weight (LMW) subunits of glutenin was characterized by investigating suboptimally stored wheat samples in situ by X-ray absorption near edge structure (XANES) spectroscopy and baking tests. Field fungi of the genus Fusarium have hardly any influence on both the sulfur speciation of wheat gluten proteins and the baking properties, whereas storage fungi of the genera Aspergillus and Penicillium have a direct influence. An increased amount of sulfur in sulfonic acid state was found, which is not available for thiol/disulfide exchange reactions in the gluten network, and thus leads to a considerably reduced baking volume. From changes of the composition of the mould flora during suboptimal storage of wheat and from the mycotoxin contents, it can be concluded that microbial competitive interactions play an important role in the development of the mould flora and the mycotoxin concentrations during (suboptimal) storage of wheat.

Aspergillus↗

Factors in virulence expression and their role in periodontal disease pathogenesis.

The classic progression of the development of periodontitis with its associated formation of an inflammatory lesion is characterized by a highly reproducible microbiological progression of a Gram-positive microbiota to a highly pathogenic Gram-negative one. While this Gram-negative microbiota is estimated to consist of at least 300 different microbial species, it appears to consist of a very limited number of microbial species that are involved in the destruction of periodontal diseases. Among these "putative periodontopathic species" are members of the genera Porphyromonas, Bacteroides, Fusobacterium, Wolinella, Actinobacillus, Capnocytophaga, and Eikenella. While members of the genera Actinomyces and Streptococcus may not be directly involved in the microbial progression, these species do appear to be essential to the construction of the network of microbial species that comprise both the subgingival plaque matrix. The temporal fluctuation (emergence/disappearance) of members of this microbiota from the developing lesion appears to depend upon the physical interaction of the periodontal pocket inhabitants, as well as the utilization of the metabolic end-products of the respective species intimately involved in the disease progression. A concerted action of the end-products of prokaryotic metabolism and the destruction of host tissues through the action of a large number of excreted proteolytic enzymes from several of these periodontopathogens contribute directly to the periodontal disease process.(ABSTRACT TRUNCATED AT 250 WORDS)

Gram-Negative Bacteria↗

Mathematical simulation and analysis of cellular metabolism and regulation.

MOTIVATION: A better understanding of the biological phenomena observed in cells requires the creation and analysis of mathematical models of cellular metabolism and physiology. The formulation and study of such models must also be simplified as far as possible to cope with the increasing complexity demanded and exponential accumulation of the metabolic reconstructions computed from sequenced genomes. RESULTS: A mathematical simulation workbench, DBsolve, has been developed to simplify the derivation and analysis of mathematical models. It combines: (i) derivation of large-scale mathematical models from metabolic reconstructions and other data sources; (ii) solving and parameter continuation of non-linear algebraic equations (NAEs), including metabolic control analysis; (iii) solving the non-linear stiff systems of ordinary differential equations (ODEs); (iv) bifurcation analysis of ODEs; (v) parameter fitting to experimental data or functional criteria based on constrained optimization. The workbench has been successfully used for dynamic metabolic modeling of some typical biochemical networks (Dolgacheva et al., Biochemistry (Moscow), 6, 1063-1068, 1996; Goldstein and Goryanin, Mol. Biol. (Moscow), 30, 976-983, 1996), including microbial glycolytic pathways, signal transduction pathways and receptor-ligand interactions. AVAILABILITY: DBsolve 5. 00 is freely available from http://websites.ntl.com/ approximately igor.goryanin. CONTACT: gzz78923@ggr.co.uk

Algorithms↗

Cross-Kingdom Siderophores: Biosynthesis, Ecology, and Biotechnological Applications.

Microbial siderophores are high-affinity iron-binding compounds which are produced by bacteria, fungi, and actinomycetes to obtain iron and survive and interact with different species in an iron-deficient environment. While the conventional research on siderophore systems deals mainly with the study within the same taxa, modern researchers have increased their inclination toward cross-kingdom integration of siderophore behavior and their impact on host-associated environments. This can be largely attributed to differences in biosynthetic gene clusters, receptor systems, and regulatory networks, which produce distinct genotype-to-phenotype results determining microbial cooperation and competition. Current advancements in genomic research, together with omics studies like transcriptomics, proteomics, and metabolomics, have created newer insights into how siderophores function. However, the present literature evidences multiple major gaps in multi-omics data because the link between genomes and metabolomes remains weak due to inconsistent regulatory data sets and failure in identifying producer-consumer relationships in polymicrobial systems. Additionally, major constraints like molecular instability, delivery system limitations, host toxicity, limitations in upscaling, and regulatory issues delimit the use of siderophores in medical treatment, agricultural practices, and environmental biotechnology. This review aims to bridge the existing knowledge about siderophore biochemistry, biosynthesis, ecological functions, and genetic regulation across kingdoms while integrating multi-omics outlook with translational considerations. Thus, by connecting molecular mechanisms with evolutionary cross-talk, this study aims to provide a system-level framework in the world of siderophore-mediated iron uptake and therefore shapes future directions in emerging fields of microbial engineering, precision therapies, and sustainable biotechnology.

Fur regulation↗