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At least 19 recordsLinked to original sources

PubMed2026

Decoding microbial metabolic complementarity from individual traits to community structuring.

A fundamental challenge in microbiome research lies in elucidating the functional capacity of microbial communities through community membership and genomic data. As community structuring and emergent functional traits are determined by bacterial community metabolic networks, it is important to gain insights into the principles that govern bacteria-bacteria interactions. Here, we applied an integrative framework linking individual strain-level traits to community structuring in a simplified synthetic bacterial community (SSC8) that promotes the growth of ungrafted watermelon. By combining mono- and coculture assays with genome-scale metabolic modeling and metabolomic profiling of spent media, we characterized directional interactions and resource dependencies among community members. Our findings show that positive interactions dominated the community network, accounting for 55% of all pairwise combinations, indicating a high prevalence of growth-promoting effects among strains. Genome-scale metabolic modeling showed that functional divergence among strains enhanced the potential for metabolic complementarity as phylogenetic distance increased. Integrating metabolic modeling with metabolomics further suggested that Pseudomonas azotifigens Q6 not only benefited from all other community members, but also exhibited mutualistic interactions with the other three strains, with metabolite exchange involving compounds such as L-lysine and L-cysteine. Pseudomonas azotifigens Q6 acted as an important driver of community composition by affecting the abundance of several other consortium members in vitro. These findings highlight the role of metabolic complementarity in driving community structuring by promoting selective persistence of specific strains. Our work provides mechanistic insights into microbial interaction networks in vitro and offers a conceptual foundation for the rational design of functionally robust and plant-beneficial microbiomes.

Bacteria↗

PubMed2026

Amplicon and metagenomic sequencing reveal thifluzamide drive rhizosphere microbial structural shifts and functional adaption.

Thifluzamide (TF) is a widely used phenyl urea fungicide in rice production; however, its impacts on the structural composition and functional dynamics of the rhizosphere microbiome remain poorly understood. Here, we systematically investigated the effects of TF on the structure, interactions, and functional potential of the rice (Oryza sativa L.) rhizosphere microbiome using integrated amplicon sequencing and metagenomic approaches. TF application significantly altered both bacterial and fungal community composition, bacterial diversity was markedly reduced, whereas fungal diversity increased. With bacterial diversity markedly reduced while fungal diversity increased. Beta-diversity analyses revealed strong treatment-driven community separation, indicating pronounced TF-induced microbial restructuring. Co-occurrence network analysis demonstrated reduced complexity and connectivity in bacterial networks but increased negative co-occurrence patterns within fungal communities, suggesting contrasting stability responses between microbial kingdoms. Metagenomic profiling further revealed substantial functional shifts, including the differential enrichment of KEGG and COG pathways associated with xenobiotic metabolism. Notably, while total ARG abundance remained stable, TF exposure altered the resistome profile by selectively enriching specific classes of antibiotic resistance genes (ARGs), biocide resistance genes (BRGs), and mobile genetic elements (MGEs). Strong positive correlations between MGEs and ARGs highlighted an elevated potential for horizontal gene transfer. Metagenome-assembled genome (MAG) analysis identified specific TF-enriched bacterial taxa, including Methylophilus, Sulfurospirillum, and Azospirillum, which harbored genes involved in pesticide degradation and xenobiotic transformation. Collectively, these findings demonstrate that TF profoundly reshapes the rice rhizosphere microbiome by altering microbial diversity, interaction networks, resistance gene profiles, and functional capacities. This study provides genomic insights into fungicide-microbiome interactions, underscoring the potential ecological implications associated with TF application, while identifying candidate microbial taxa that may contribute to pesticide degradation and rhizosphere microecology resilience.

Rhizosphere↗

PubMed2026

Oral bacteriome in pediatric patients with malignancies prior to chemotherapy: a pilot study using full-length 16S rRNA sequencing.

OBJECTIVE: To characterize the composition, diversity, and ecological features of the oral bacteriome in pediatric patients with malignancies prior to chemotherapy initiation. METHODS: In this prospective pilot study,supragingival plaque samples were collected from 10 pediatric cancer patients prior to the initiation of chemotherapy. Bacterial genomic DNA was extracted from each sample, and the full-length 16S rRNA gene was amplified and sequenced on the PacBio Sequel II platform using circular consensus sequencing (CCS). Raw CCS reads were quality-filtered and denoised into amplicon sequence variants (ASVs) using DADA2, and taxonomic assignment was performed against the SILVA 138 reference database. Alpha diversity was assessed using the Chao1, Shannon, Simpson, and Faith's phylogenetic diversity (PD whole tree) indices, while beta diversity was evaluated through principal coordinate analysis (PCoA), and non-metric multidimensional scaling (NMDS). Microbial co-occurrence networks were constructed to characterize bacterial interactions, and functional potential was predicted using PICRUSt2, and BugBase. RESULTS: A total of 614,473 high-quality CCS reads were generated, yielding 1,697 ASVs. Alpha diversity analysis revealed substantial inter-individual variation in microbial richness and diversity among the pediatric cancer patients. The bacterial community was dominated by the phyla Firmicutes, Proteobacteria, Bacteroidota, Actinobacteriota. At the genus level, Streptococcus, Prevotella, Neisseria, and Haemophilus were the most abundant taxa. Beta diversity analysis revealed distinct clustering patterns, indicating highly individualized microbial profiles. Co-occurrence network analysis identified several keystone taxa and potential pathogenic associations within the supragingival plaque community. Functional prediction indicated that the dominant metabolic pathways were related to amino acid metabolism, carbohydrate metabolism, and membrane transport. CONCLUSION: These preliminary findings reveal a taxonomically diverse, highly individualized pre-chemotherapy oral bacteriome, providing foundational baseline profiles to guide future longitudinal investigations of chemotherapy-induced dysbiosis and personalized interventions.

Humans↗

PubMed2026

Integrative machine learning models to unravel gut microbial dysbiosis and functional disruption in polycystic ovary syndrome.

OBJECTIVE: To study gut microbial diversity and metabolic pathway disruptions in women with PolyCystic Ovary Syndrome (PCOS) compared with healthy controls, and to evaluate the diagnostic potential of microbiome-driven machine learning models. DESIGN: Case-controlled metagenomic data analysis SUBJECTS: Gut metagenomic data from women diagnosed with PCOS and age-matched healthy female controls EXPOSURE: Presence of PCOS MAIN OUTCOME MEASURES: The primary outcome measures will include gut microbial alpha and beta diversity indices, microbial taxon abundance, functional pathway profiles, predicted metabolite levels, microbe-functional pathway-metabolite interaction networks, and the diagnostic accuracy of microbiome-based machine learning models. RESULTS: Alpha and beta diversity analyses revealed marked gut microbial dysbiosis in women with PCOS, despite comparable species richness to healthy controls. Differential abundance analysis identified 41 significantly altered microbial species, including enrichment of proinflammatory taxa, such as Bacteroides vulgatus and Ruminococcus gnavus, and depletion of beneficial commensals, including Roseburia hominis and Prevotella copri. These compositional shifts indicate a proinflammatory microbial community structure in PCOS. Functional profiling demonstrated the upregulation of pathways involved in nucleotide turnover, lipid and carbohydrate metabolism, and neurotransmitter synthesis, potentially contributing to metabolic and neuroendocrine disruption. Network analysis revealed fragmented and unstable microbial-metabolite associations in PCOS compared with cohesive networks in controls. Microbiome-based machine learning models achieved a diagnostic accuracy of 84.25% (area under the curve 0.93), underscoring their predictive potential. CONCLUSION: The gut microbiome in PCOS is characterized by a proinflammatory community structure and disrupted metabolic pathways. These findings demonstrate the diagnostic potential of microbiome-based models and underscore the gut microbiome as a promising target for therapeutic interventions in the management of PCOS.

Polycystic Ovary Syndrome↗

PubMed2026

Metagenomic Insights into Microbial Assembly and Key Metabolic Genes Driving Flavor Formation in Spontaneously Fermented Zhejiang Rosy Vinegar.

The spontaneous fermentation of Zhejiang rosy vinegar (ZRV) is driven by environmental microbiota, but the processes underlying its flavor formation remain poorly understood. Using metagenomic sequencing, we investigated microbial community assembly, environmental drivers, and metabolic networks during industrial-scale ZRV fermentation. Acetic acid dominated the final organic acids. Community assembly shifted toward deterministic selection with rising acidity, with a slight rebound of stochastic processes in the late stage (R2 values of 0.442 and 0.346 for bacteria and fungi, respectively). Mantel tests confirmed that environmental factors significantly regulated microbial assembly. Co-occurrence networks grew more complex, with positive interactions accounting for 85.24% (bacteria) and 90.10% (fungi) in the late stage. Key genes (ldh, gapA, pgk) from Acetobacter pasteurianus and Lactobacillus acetotolerans dominated late-stage fermentation, while genes (adhP, SDH) from Aspergillus oryzae and Saccharomyces cerevisiae supported early- and mid-stage fermentation. These findings elucidate microbiota-driven metabolic pathways in ZRV, supporting the fermentation window optimization and industrial vinegar quality standardization.

Acetic Acid↗

PubMed2026

Analysis of the dual role of amyloid-beta in Alzheimer's disease through multi-omics integration.

Accumulation of amyloid-beta is highly important in the development of Alzheimer's disease. Given the limitations of the amyloid cascade hypothesis and the repeated clinical failures of anti-amyloid-beta therapies, researchers are increasingly exploring the infection hypothesis. This review explores the dual behaviors of amyloid-beta in Alzheimer's disease, with a particular focus on its protective role against infection by microorganisms and its complicated connections with innate immune system. This new opinion holds that amyloid-beta can play an antimicrobial peptide role. During microbial invasion, its original role is to protect neural tissue, but prolonged accumulation leads to chronic deposition and involvement in pathological processes. Evidence from in vitro experiments, animal models, and clinical studies indicates that amyloid-beta may possess antiviral and antibacterial properties, particularly against infections such as herpes simplex virus, human immunodeficiency virus, and Porphyromonas gingivalis . However, excessive accumulation of amyloid beta triggers a neuroinflammatory cascade that impairs neuronal regeneration and cognitive function. Despite substantial research into Alzheimer's disease, current treatments have not yielded significant clinical benefits. Although monoclonal antibodies such as Aducanumab , Lecanemab , and Donanemab have been approved for marketing, their strict indications and high costs pose challenges for widespread promotion. The infection hypothesis of amyloid-beta has spurred clinical trials investigating vaccines targeting specific pathogens to assess their potential in preventing or treating Alzheimer's disease. This highlights the need for further exploring the multifaceted role of amyloid-beta in Alzheimer's disease. In addition, microbial infections can also trigger or regulate genetic and epigenetic factors, accelerating amyloid beta deposition. Among them, the apolipoprotein E epsilon 4 allele is the strongest genetic risk factor for Alzheimer's disease, as it exacerbates the accumulation of amyloid beta and promotes neuroinflammation. Strategies targeting epigenetic regulation may provide novel approaches to inhibit Alzheimer's disease pathology. This review also integrates various technologies such as genomics, proteomics, and metabolomics. This provides a broader system-level understanding of the risk gene loci, protein interaction networks, and metabolic changes associated with amyloid beta under the influence of microbial infections. Such techniques may lead to the identification of new molecular targets, the development of individualized treatment strategies, and the creation of early biomarkers for use in clinical research. In conclusion, this review suggests that amyloid-beta is not merely a pathological by-product but an environmentally responsive molecule with dual functions. A deeper understanding of the dynamic regulation of amyloid-beta, considering infection status and disease stage, can provide new directions for treatment strategies aimed at the prevention and treatment of Alzheimer's disease.

Herpesvirus 1↗

PubMed2024

Effects of initial corncob particle size on the short-term composting for preparation of cultivation substrates for Pleurotus ostreatus.

The short-term composting based on corncob for preparing Pleurotus ostreatus cultivation medium originated from agricultural production practices and so lacked systematic investigation. In this study, the influences of a Dafen (15 mm, DFT) and Xiaofen (5 mm, XFT) initial particle size (IPS) of corncob on the microbial succession and compost quality were examined. Results demonstrated that XFT compost was better suited for mushroom cultivation due to its high biological efficiency of 70 % and the absence of contamination. The composting microbes differed significantly between the DFT and XFT composts. During composting, the genera of Bacillus, Acinetobacter, Lactobacillus, Streptomyces, and Paenibacillus were majorly found in the DFT compost, while Acinetobacter, Lactobacillus, Puccinia, Bacteroides, and Bacillus genera dominated the XFT compost. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that throughout the thermophilic phase, XFT compost had much greater relative abundances of sequences relevant to energy, carbohydrate, and amino acid metabolism than DFT compost. Analysis of network correlations and Mantel tests indicated that IPS reduction could increase microbial interactions. Overall, adjusting the IPS of corncob to 5 mm increased microbial interactions, improved compost quality, and thereby boosted the P. ostreatus yield. These findings will be pertinent in optimizing the composting process of cultivation medium for P. ostreatus.

Composting↗

PubMed2026

Unraveling critical role of photosynthetic bacteria in sustaining aquatic microbial community stability and function through large-scale genomic data analyses.

The application of photosynthetic bacteria (PB) in water remediation has demonstrated exceptional advantages in terms of high efficiency and low-carbon benefits. However, the limited understanding of PB across natural aquatic environments has constrained the rational development of this strategy. Here, we analyzed 3198 genomic sequencing samples from seven types of natural aquatic ecosystems to investigate the distribution and functions of 42 PB genera commonly used in water remediation. The results showed that the average abundance of the targeted PB reached 9.83 %, with the highest value of 14.93 % observed in River, while Lake harbored the greatest PB genus diversity. PB genera exhibited high sensitivity to salinity, with Rhodoferax dominating freshwater habitats, whereas Rhodovulum was predominant in marine environments. Notably, co-occurrence network analysis revealed that PB were closely associated with microbial community stability and optimized interspecific interactions. Aquatic microbial communities with high PB abundance were characterized by efficient division-of-labor modules, accompanied by enhanced PB-associated functional potential for carbon fixation, denitrification, and sulfur oxidation. In summary, this study systematically elucidates the regional biogeographical patterns and ecological roles of PB in natural aquatic environments, providing a comprehensive scientific basis and theoretical guidance for the development and practical application of PB-based water remediation technologies.

Bacteria↗

PubMed2026

In silico analysis and comparison of the metabolic capabilities of different organisms by reducing metabolic complexity.

BACKGROUND: Understanding how metabolic capabilities diverge across microbial species is essential for deciphering community function, ecological interactions, and the design of synthetic microbiomes. Despite shared core pathways, microbial phenotypes can differ markedly due to evolutionary adaptations and metabolic specialization. Genome-scale metabolic models (GEMs) provide a systems-level framework to explore these differences; however, their complexity hinders direct comparison. RESULTS: We introduce NIS (Neidhardt-Ingraham-Schaechter), a computational workflow that integrates the redGEM, lumpGEM, and redGEMX algorithms to systematically reduce genome-scale models into biologically interpretable modules. This approach enables direct, quantitative comparison of fueling pathways, biomass biosynthetic routes, and environmental exchange processes while retaining essential metabolic information. We first demonstrate the utility of NIS by analyzing Escherichia coli and Saccharomyces cerevisiae, which revealed both conserved and divergent strategies in central metabolism, biosynthetic cost, and substrate utilization. We then applied NIS to the core honeybee gut microbiome, uncovering distinct metabolic traits, functional redundancy, and complementarity that help explain auxotrophy, cross-feeding interactions, and microbial coexistence. CONCLUSIONS: NIS provides an automated, scalable, and reproducible framework for dissecting microbial metabolic networks beyond gene content or taxonomy. By linking metabolism to ecological function, NIS offers new opportunities to interpret microbial community dynamics and to support the rational design of microbiomes in health, agriculture, and environmental applications. Video Abstract.

Metabolic Networks and Pathways↗

PubMed2026

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↗

PubMed2026

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↗

PubMed2022

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↗

PubMed2025

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↗

PubMed2026

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↗

PubMed2025

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↗

PubMed2026

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↗

PubMed2026

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↗

PubMed2026

Microbial partnerships and molecular mechanisms in plant stress physiology for climate-resilient and sustainable farming.

Plant-microbial partnerships and their underlying molecular mechanisms are indispensable, natural drivers of improved nutrient acquisition and stress tolerance in the face of climate-driven environmental challenges. Modern multi-omics tools, when coupled with artificial intelligence and synthetic biology, enable the precise design of targeted bioinoculants and synthetic microbial consortia. Translating these advanced microbiome-based strategies into scalable, field-level agricultural applications provides a sustainable path toward securing global food production while maintaining soil health. Global climate change imposes multifaceted abiotic and biotic stresses on crops, disrupting physiological and molecular processes and threatening agricultural productivity. Plant-associated microbes represent an underexplored yet powerful ally in enhancing crop resilience. This review presents current knowledge of plant-microbe interactions and the molecular mechanisms governing plant stress physiology, with an emphasis on climate-resilient and sustainable farming. Hence, ever-changing environmental cues pose a significant burden on agricultural productivity, and plant-associated microbial communities modulate a cascade of physiological and molecular responses, including production of phytohormones, signaling, regulation of reactive oxygen species homeostasis, and activation of plant immune responses to help plants withstand stress and enhance productivity. Moreover, root exudates, phytohormones, and quorum sensing mediate the central communication networks, facilitating plant-microbe cross talk. Additionally, the advances in OMICs approaches aid in disentangling the molecular underpinnings of these interactions by providing mechanistic insights and potential candidate gene targets for crop improvement and stress resilience. In the post-genomic era, integrating artificial intelligence and big data analysis to optimize microbiome-based strategies for sustainable agriculture is a new frontier for disentangling plant-microbe symbiosis to improve soil health, enhance crop yields, and improve stress tolerance. Thus, by integrating the ecological, physiological, and molecular perspectives, this review highlights the transformative potential of harnessing plant-microbe symbiosis for climate-resilient and sustainable agriculture.

Stress, Physiological↗