BioSciences

Abstract Bacterial microcompartments (BMCs) are self-assembling organelles that colocalize enzymes and metabolites of various biochemical pathways. Their selectively permeable shell is made entirely of protein, and the inherent properties of these systems make them attractive templates for bioengineering synthetic nanoreactors. Understanding the extent of shell stability in an extended range of conditions broadens the scope of chemistries that can be confined in these nanocompartments. Here we analyze the stability of in vitro assembled BMC shells and find that they retain their structure at high concentrations of various solvents and detergents, at temperatures up to 75 °C, and across a pH range from 3.9 to 11. Shell disassembly is observed below pH 3.9, but shells can be reassembled upon increasing the pH back to 8. These results expand the range of reactions that can be performed inside protein-based nanoreactors and underscore the potential of modular BMC shell proteins as versatile biomaterials.

Interactions between microbes shape the structure and function of microbial communities. While studying interactions is key to understanding microbial communities as a whole, gaining a detailed mechanistic view is challenging due to the number of co-occurring interactions. Here, we focus on a single class of shared nutrients, the cobalamin (vitamin B₁₂) family of enzyme cofactors known as cobamides, to study nutrient interactions in laboratory consortia of increasing complexity. Measuring cobamide-dependent growth of two soil Caulobacter isolates in monoculture revealed that each grows faster within a distinct range of cobamide concentrations. Co-culturing the two isolates demonstrated that the cobamide concentration predictably determines which isolate predominates. These results suggest that, although the two organisms appear functionally redundant, with identical metabolic capabilities predicted in their genomes, they may occupy distinct niches in their environment based on cobamide concentration. We further examined the ability of cobamide-producing Mesorhizobium and Priestia isolates to share cobamides and found that both support the cobamide-dependent growth of the two Caulobacter isolates. The Caulobacter isolates could coexist in tri-cultures with each producer following serial passaging, indicating the producers can support the growth of multiple cobamide-dependent organisms. We analyzed the metabolic capacity encoded in the genomes of the four isolates and found that cobamides are likely the main shared nutrient in our co- and tri-cultures. These results highlight the utility of cobamides as a model nutrient to characterize and predict interactions in bacterial consortia of increasing complexity.IMPORTANCEInteractions between microbes shape their communities and the world around them. Because microbial communities are physically and chemically complex, predicting microbial interactions is often difficult. Using a set of four bacteria isolated from the same soil environment, we generated and tested predictions about the interactions involving a family of model nutrients related to cobalamin (vitamin B₁₂). Understanding and predicting interactions can lead to greater insights into microbiome function.

Klebsiella pneumoniae is a WHO critical-priority pathogen for which strain-specific bacteriophages are being explored as precision antimicrobials, yet rapid phage-host matching remains a major barrier to therapeutic deployment. We constructed a comprehensive interaction atlas comprising 84 taxonomically diverse phages and 101 globally sourced, clinically representative K. pneumoniae strains, including multidrug-resistant isolates. Systematic pairwise profiling produced 8,484 interaction measurements, of which 2,656 (31.3%) scored positive for bacterial clearance. Genus was the dominant phage-side determinant of host range, while capsule K-serotype was the strongest host-side determinant of susceptibility; aggregate defense, prophage, plasmid, and antimicrobial-resistance features contributed comparatively little. A genome-guided machine learning model predicted interactions without curated host annotations (AUROC, 0.882; AUPR, 0.765), outperforming a model based only on phage genus and K-serotype and modestly exceeding a curated genomic baseline. The model recovered capsule- and lipopolysaccharide-biosynthesis genes, canonical receptors and defense-associated features as major predictors using SHAP analysis. Feasibility tests of expert- and model-selected cocktails exposed a translational constraint. Although all formulations suppressed growth in vitro , only the specific cocktail whose phages replicated robustly within the murine gut reduced colonization, suggesting in vivo amplification rather than predicted host range as the limiting factor for therapeutic efficacy. Together with the activity of a model-selected cocktail built for an isolate completely excluded from training, these results provide a species-wide resource for K. pneumoniae phage matching and support a hybrid workflow combining genome-based ranking with targeted phenotypic validation.

Dissolved organic matter (DOM) is a central component of Earth’s carbon cycle and one of the planet’s most chemically diverse pools, yet the molecular structures of its constituents remain largely unresolved. This limitation has hindered our ability to link DOM composition to microbial processes and ecosystem function. Here we present ENVnet, a global molecular repository built from tandem mass spectrometry data collected across 13 terrestrial and aquatic environment types, including 419 newly generated samples that expand publicly available DOM metabolomics data and cover previously underrepresented environments. By computationally deconvolving chimeric mass spectra, a longstanding challenge in environmental metabolomics, we recover high-quality fragmentation data for >22,000 distinct molecular features (defined by a specific precursor mass and fragmentation pattern). Using ENVnet, we uncover conserved and environment-specific molecular patterns in DOM composition and underlying biogeochemical processes. We also use molecular features encoded in ENVnet to train predictive models of DOM persistence, allowing molecular-level assessment of microbial turnover in independent systems.

The interrogation of data across biological and environmental systems has become increasingly complex. Fortunately, communities are adopting the FAIR (Findable, Accessible, Interoperable, Reusable) data principles for individual datasets, and continue to develop domain-specific, machine-actionable standards. However, integrating FAIR data for meta-analysis across data resources is still challenging. Understanding how disparate datasets are organized remains a manual, time-consuming process. Updating FAIR databases to reflect changes in knowledge is slow, allowing stale annotations and incorrect relationships to propagate, amplified by Artificial Intelligence (AI) systems that harvest data. Building on FAIR, we argue that data should be iteratively updated and improved. FAIR + COPE (Comparable, Organized, Predictive, Engaged) takes FAIR data and makes it Comparable, rapidly Organized (applying / updating standards) for Predictive models, which can be validated and improved by an Engaged community. We provide examples of FAIR + COPE resources and science use cases that highlight the importance of FAIR + COPE in scientific research.

Forecasted increases in drought events resulting from climate change pose a serious threat to bioenergy crop yields, and improving the resilience of crops against water stress is essential to bioenergy development. Many plants exhibit a conserved drought response that is partially mediated by their associated microbiomes. Panicum hallii , a close relative of the promising bioenergy crop switchgrass, is a model organism for this grass due to its genetic tractability and short generation time. Nevertheless, little is known regarding the effects of drought on its associated microbiome. A comprehensive analysis of bacterial and archaeal microbiomes of drought-exposed P. hallii combining 16S rRNA amplicon and shotgun metagenomic data supports a drought response similar to that observed in other crops and offers new insights. Over the course of one growth cycle, exposure of P. hallii to drought led to a decrease in bacterial diversity in the rhizosphere-associated microbiome, corresponding with a decrease in Proteobacteria (Pseudomonadota) and Bacteroidota and an increase in Actinobacteriota (Actinomycetota). Genome-resolved metagenomic analysis further revealed an enrichment in Patescibacteria and specific Bacteroidota under drought. Functional analysis linked the changes in community structure with increased capacities for complex carbohydrate metabolism, molecular salvaging, chemotaxis, and antioxidant accumulation. By confirming that drought stress effects similar changes to the P. hallii rhizosphere microbiome as observed in switchgrass and other plants, we reinforce the strength of P. hallii as a model organism for crop improvement studies and emphasize the value of a dual amplicon and shotgun-based analysis for a comprehensive interpretation of community composition. Copyright © 2026 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Microbial methanogenesis is a major contributor to global warming, and methane fluxes represent a loss of energy and electrons from industrial ecosystems. The chemical space of methane control strategies is still underexplored. Most known methanogenesis inhibitors target methanogenic archaeal enzymes. However, interference with the exchange of syntrophic electron carriers (H₂ or formate) in methanogenic systems presents an additional target for methane control. Here, we show that hypophosphite (H₂PO₂^-), an inorganic formate analogue, is a potent and selective inhibitor of syntrophic methanogenesis versus primary fermentation in rice field sediments and cattle rumens. Hypophosphite is also generally recognized as safe and relatively nontoxic to plants and animals. Genetic screens and physiological assays in the model methanogen Methanococcus maripaludis S2 implicate formate metabolism as the target of hypophosphite inhibition. Currently, there is no known biological pathway for anaerobic hypophosphite oxidation, and hypophosphite is stable in anoxic sediments for weeks to months. Given its widespread natural occurrence, we propose that hypophosphite may modulate the carbon cycle in natural environments. Taken together, our results suggest that hypophosphite could be used as a safe, inexpensive strategy for methane control in syntrophic methanogenic ecosystems.

BACKGROUND: The integration of many disparate forms of data is essential for understanding the microbial world and its interaction with the environment and human health. Doing so is particularly challenging in the context of microbe-host and microbe-microbe interactions that contribute to health or environmental outcomes. There are thousands of relevant microbial species, and millions of interactions among those microbes and with their environment or host. Integrated information (e.g., about host and microbial physiology, genetics, and metabolism) facilitates deeper understanding of complex mechanisms and helps interpret correlative results. RESULTS: The KG-Microbe construction framework is a novel approach to harmonizing bacterial and archaeal data in the form of a Findable, Accessible, Interoperable, Reusable (FAIR) and AI-ready knowledge graph (KG). Starting from a core KG with organismal traits, environments and growth preferences and the integration of established ontologies, the framework generates a hierarchy of related KGs targeting specific use cases, including the human microbiome in the context of disease, or environmental microbiomes. The framework supports customizable taxa subsets representing communities or clades of interest. Evaluations of the KG-Microbe KGs through a series of competency questions demonstrate the accuracy and effectiveness of the data harmonization, and the utility of the resulting KGs in studies of inflammatory bowel and Parkinson's diseases. Finally, the predictive and environmental capabilities of the KGs are demonstrated by explaining growth preferences using graph features. CONCLUSIONS: The KG-Microbe framework unifies microbial contexts in a single resource to support integrative analyses across biomedical, host, and environmental domains. KG-Microbe is a flexible, modular enabling technology for humans and machine learning methods to uncover mechanistic explanations of microbial associations.

Background: Recurrent neonatal infection caused by the same bacterial lineage across pregnancies is rarely documented. We describe two preterm siblings born one year apart who developed Escherichia coli sepsis caused by clonally related strains. Case Summary: Two female infants were born preterm, 1 year apart, at 26 weeks and 4 days and 30 weeks and 3 days of gestation, respectively, following prolonged premature rupture of membranes. Both infants were evaluated for early-onset sepsis at birth with blood cultures and administered empiric ampicillin and gentamicin; initial blood cultures were sterile, and antibiotics were discontinued after 24 h. At 7 days of age, each infant developed new clinical signs of infection, and both were diagnosed with E. coli bacteremia. Both infants recovered after treatment with intravenous antibiotics. Whole-genome sequencing identified the isolates as O15:K52:H18, sequence type 69 (ST69), differing by only a few single nucleotide polymorphisms. Conclusion: These cases suggest recurrent vertical transmission of a pathogenic E. coli lineage leading to neonatal infection arising just beyond the immediate newborn period.

In Arabidopsis (Arabidopsis thaliana) roots, growth initiation and cessation are organized into distinct zones. How regulatory mechanisms are integrated to coordinate these processes and maintain proper growth progression over time remains poorly understood. Here, we demonstrate that the peptide hormone PLANT PEPTIDE CONTAINING SULFATED TYROSINE 1 (PSY1) promotes root growth by controlling cell elongation. Higher levels of PSY1 lead to longer differentiated cells with a shootward displacement of characteristics common to mature cells. PSY1 activates genes involved in the biosynthesis of flavonols, a group of plant-specific specialized metabolites. Consistent with these transcriptional changes, metabolomic analysis reveals an enrichment of diverse flavonol glycosides upon PSY1 treatment. Using genetic and chemical approaches, we show that PSY1-mediated flavonol accumulation is localized to the differentiation zone and is required for PSY1 function. PSY1 signaling in this zone is associated with a reduction of hydrogen peroxide accumulation and a decrease in auxin-induced gene expression. These findings support a model where PSY1 signals the developmental-specific accumulation of specialized metabolites to regulate the extent of cell elongation and progression to maturation.