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Plaque assays remain the gold standard for bacteriophage quantification, but routine plaque counting is labor-intensive, time-consuming, and poorly suited to large experiments or automated workflows. Conventional plaque images also often provide insufficient contrast for simple software-based counting, especially when plaques are small, faint, or heterogeneous. Here we show that a membrane-impermeant nucleic acid dye can convert standard bacteriophage plaque assays into a high-contrast, machine-readable format compatible with simple automated counting. In a soft-agar overlay workflow, fluorescent labeling enabled plaque detection and automated enumeration using an open-source ImageJ pipeline based on Find Maxima, without phage engineering, machine learning, or custom software. Because the method improves the image contrast of the assay itself, it may also provide improved input for future machine-learning or other advanced automated counting workflows. The method was evaluated across diverse phage-host systems spanning dsDNA, ssRNA, filamentous, and enveloped phages, including T7, MS2, M13, and phi6. In lytic systems, fluorescent signal emerged prior to or alongside conventional plaque visibility and yielded automated counts that agreed closely with manual counting. M13 exhibited delayed fluorescence consistent with its chronic, nonlytic lifestyle, yet remained machine-countable at the conventional next-day endpoint. A Gram-positive Leo2-Bacillus safensis system revealed an important compatibility limit: dye incorporation at plating inhibited plaque formation, but a post-labeling workflow restored detectability and automated counting. Together, these results show that membrane-impermeant dye labeling can make plaque assays more computationally tractable while preserving the accessibility of standard phage methods. This approach provides a practical path toward higher-throughput, statistically rigorous phage biology in both low-resource and automation-oriented laboratories.

Streptococcus pneumoniae asymptomatically colonizes the nasopharynx but can invade the lower respiratory tract to cause life-threatening disease, particularly in older adults. However, whether the initial site of bacterial deposition following intranasal inoculation determines disease progression has not been directly examined. Here, we developed a near-infrared (NIR) fluorescence imaging approach using indocyanine green (ICG)-labeled S. pneumoniae TIGR4 to visualize early bacterial distribution in real time. ICG labeling by simple mixing, without genetic or chemical modification, neither impaired bacterial growth at 33 or 37{degrees}C, nor altered acid tolerance. Continuous video imaging during the first 10 min of infection resolved two distinct patterns: bacteria confined to the nasopharynx (colonization) and those aspirated into the lower respiratory tract (aspiration). Kaplan-Meier analysis revealed markedly higher mortality in the aspiration group in both young (hazard ratio = 7.9) and aged (hazard ratio = 8.4) mice, despite a 10-fold lower inoculum used for aged animals, with deaths beginning on day 3. Systemic profiling of blood at 24 h by RNA sequencing and plasma proteomics revealed that early aspiration in aged mice was associated with the activation of inflammatory and hematopoietic programs, enrichment of complement and coagulation cascades, and phagocytic pathways. Together, these findings establish aspiration into the lower respiratory tract as a trigger of severe pneumococcal disease and introduce real-time NIR imaging as a technique for linking early infection dynamics to systemic host responses.

The rise of antimicrobial resistance has intensified interest in antimicrobial photodynamic inactivation (aPDI) and antimicrobial blue light (aBL) as alternatives or adjuvants to conventional antibiotics. However, whether chemically distinct photodynamic treatments elicit a shared bacterial response remains unclear. Here, we integrated transcriptomic profiles of Escherichia coli BW25113 exposed to five short-term, sub-lethal photodynamic treatments: antimicrobial blue light (aBL), aBL combined with 5-aminolevulinic acid (aBL+ALA), rose bengal (RB), new methylene blue (NMB), and the cationic porphyrin TMPyP. Intersection analysis identified 891 conserved core genes differentially expressed across all treatments, of which approximately 98% changed in a consistent direction despite differences in photosensitizer chemistry and activating wavelength. Random-effects meta-analysis and robust rank aggregation prioritized 88 high-confidence genes, revealing induction of envelope stress and cytoplasmic protein quality control pathways alongside repression of acid resistance, hydrogen metabolism, molybdate transport, and biofilm formation. Regulon enrichment indicated that heat-shock sigma factor {sigma}32/RpoH and the envelope-stress regulators CpxR, BaeR, {sigma}24/RpoE, and PspF were enriched among induced genes, whereas GadW/GadX/GadE, Fur, and {sigma}38/RpoS were enriched among repressed genes. Functional validation using selected single-gene Keio knockouts confirmed that deletion of conserved-core genes sensitized E. coli to photodynamic treatment and delayed post-treatment recovery in a modality-dependent manner. Moreover, RT-qPCR analysis of selected transcriptional responses confirmed the direction and overall pattern of RNA-seq-derived expression changes. Together, these findings define a unified conserved early survival program in E. coli after chemically distinct photodynamic treatments and identify stress-response modules that may serve as targets for potentiating aPDI. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/744726v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@dbbadaorg.highwire.dtl.DTLVardef@1c85538org.highwire.dtl.DTLVardef@152d699org.highwire.dtl.DTLVardef@18705a6_HPS_FORMAT_FIGEXP M_FIG C_FIG

Foodborne pathogens including Salmonella spp., Escherichia coli and Listeria monocytogenes cause an estimated 600 million illnesses annually. Yet conventional detection methods remain slow, costly, or insufficiently specific for routine food safety surveillance. Phage receptor-binding proteins (RBPs) are attractive recognition elements for biosensors, but their extensive sequence diversity limits reliable computational identification. Here, we present a systematic open-source computational pipeline for identifying and structurally characterising high-confidence RBP candidates from phage genomes targeting these three priority pathogens. The pipeline integrates four stages: deep learning-based RBP prediction, protein structure prediction, structural homology validation, and exploratory molecular docking. Applied to a quality-controlled dataset of 247 complete phage genomes retrieved from the National Center for Biotechnology Information Nucleotide database (31,752 total protein sequences), PhageRBPdetect, built on the ESM-2 protein language model, identified 653 high-confidence RBP candidates. Foldseek structural homology validation against PDB100 confirmed 13 candidates with a structural match probability of 1.0 to known phage adsorption proteins, spanning four structural archetypes. ESMFold-predicted structures showed strong confidence, with a mean model confidence score of 0.89 and a 90.2% prediction success rate. Exploratory rigid-body docking identified YDV08491.1, an E. coli-targeting candidate, as having the most energetically favourable predicted interaction, with a predicted binding energy of -132.3 kcal/mol against OmpF, supporting experimental prioritisation. These candidates structural diversity and predicted host specificity support their future development as phage-based biosensors and biocontrol tools, and this reproducible, accessible pipeline offers a transferable strategy for prioritising RBP candidates in downstream functional studies, pending experimental validation. Author SummaryFoodborne bacterial infections cause an estimated 600 million illnesses every year worldwide, with Salmonella, Escherichia coli, and Listeria monocytogenes among the most significant contributors. Detecting these pathogens quickly and specifically in food supply chains remains a major challenge for global food safety. Bacteriophages viruses that infect bacteria use surface proteins called receptor-binding proteins (RBPs) to recognize their bacterial hosts with remarkable precision, making RBPs promising building blocks for pathogen-specific biosensors. However, the huge sequence diversity of RBPs across phage genomes has made it difficult to reliably identify good candidates computationally. We built an open-source pipeline that combines deep learning, protein structure prediction, and molecular docking to screen phage genomes for high-confidence RBP candidates. Applied to 247 phage genomes targeting the three pathogens above, our pipeline flagged 653 candidate RBPs, of which 13 showed strong structural similarity to known phage adsorption proteins. One candidate, targeting E. coli, showed a particularly strong predicted binding interaction with a bacterial surface protein, making it a priority target for experimental testing. This pipeline gives researchers a reproducible, publicly available strategy for prioritizing phage proteins toward the development of biosensors and biocontrol tools for food safety.

Phage cocktails are promising biocontrol agents against bacterial plant diseases by broadening host range and limiting the emergence of resistant mutants. To date, nine lytic phages with properties suitable for biocontrol have been isolated against Xanthomonas hortorum pv. vitians, the causal agent of bacterial leaf spot of lettuce. Here, a six-phage cocktail was rationally designed based on complementary host ranges, covering 91% of tested vitians strains while maintaining strict phage specificity toward the pathovar. To design a robust biocontrol, three distinct phage infection strategies, identified by transposon insertion sequencing, were combined in a cocktail. The susceptibility determinants were involved in LPS biosynthesis, a modified O-antigen structure, and an outer membrane protein putatively linked to the type I secretion system. As these structures contribute to plant colonization and virulence, phage resistance is expected to impose substantial fitness costs. In growth-chamber experiments, the phage cocktail provided dose-dependent protection, with significant symptom reduction observed across all tested concentrations, from 17% at 106 PFU.mL-1, to 34.7% at 107 PFU.mL-1 (two applications), and up to 66% at 108 PFU.mL-1. In two independent field trials conducted across contrasting growing seasons, weekly applications consistently reduced disease severity by 30%, decreased the proportion of non-marketable lettuce heads by more than 84%, and reduced post-harvest trimming losses from 20.7% to 18.1% in summer and from 17.8% to 14.0% in autumn. These findings provide the first demonstration of a reproducible and effective phage-based biocontrol strategy against Xanthomonas hortorum pv. vitians under field conditions.

The global spread of antimicrobial-resistant (AMR) bacteria represents one of the greatest threats to modern medicine, making the development of novel therapeutic strategies increasingly urgent. Phage therapy, which exploits bacteriophages (phages, viruses that specifically infect and kill bacteria) has regained attention as a therapeutic approach for multidrug-resistant infections. One critical determinant of treatment outcome is the capacity of phages to sustain bacterial growth suppression; however, no metric has previously existed to directly quantify the duration of effective lytic activity. Here, we propose the Sustainability Index (SusI), a novel metric that quantifies both the duration and extent of phage-mediated bacterial growth suppression, which is restricted to the primary lysis period from lysis initiation to resistance emergence. Evaluation of individual phages and two-phage cocktails against both laboratory and clinical isolates of Escherichia coli demonstrated that SusI provides information independent of the Virulence Index, which primarily reflects bactericidal activity during the initial phase of infection, and serves as a complementary metric to the Suppression Index, which may incorporate behavior beyond primary lysis. Cocktails composed of phages targeting different receptors specificities consistently exhibited higher SusI values, consistent with the notion that multifaceted selective pressure delays resistance emergence. Furthermore, in a mouse model of systemic infection established by intraperitoneal administration, cocktails with higher SusI values demonstrated superior therapeutic efficacy. These results confirm SusI as a practical metric for rational phage cocktail design. As phage therapy advances toward clinical implementation, standardized quantitative metrics such as SusI are expected to facilitate evidence-based selection of therapeutic phages across diverse pathogens and infection conditions. ImportanceThe global spread of antimicrobial-resistant bacteria is making bacterial infections increasingly difficult to treat. Phage therapy, which uses bacteriophages (viruses that specifically infect bacteria), has re-emerged as a therapeutic alternative; however, reliable methods to determine in advance which phages will be therapeutically effective remain limited. Current evaluation metrics are well-suited for quantifying how rapidly phages kill bacteria but were not designed to directly measure how long lytic activity is sustained before resistant bacteria emerge. Here, we developed the Sustainability Index (SusI), a novel metric that specifically quantifies the duration of effective bacterial growth suppression. Evaluation of multiple phages and their combinations (cocktails) against both laboratory and clinical bacterial isolates demonstrated that SusI can distinguish phage combinations that existing metrics fail to differentiate. Moreover, in a mouse model of lethal bacterial infection, higher SusI values correlated with improved therapeutic outcomes. SusI has potential as a practical tool for selecting phages with greater likelihood of therapeutic success.

Quantitative microscopy of microorganisms increasingly produces large, multidimensional datasets, yet their analysis often depends on fragmented workflows spanning file conversion, segmentation, quality control, fluorescence quantification, tracking, and visualization. Here, we present BactoMate, an open-source, cross-platform graphical user interface that integrates these steps into a unified workflow for microbial image analysis. BactoMate incorporates established segmentation methods and supports both single-file and batch processing. Its modules enable image preprocessing, cell segmentation, morphology-based quality control, fluorescence and foci quantification, single-cell tracking, lineage reconstruction, structured data export, and generation of quality-control and visualization outputs. We demonstrate the applicability of BactoMate across multichannel fluorescence imaging, bacterial swimming assays, microcolony lineage analysis, phage infection assay and a microfluidic time series. All user-configurable parameters are exposed through the interface, are recorded alongside structured outputs and can be loaded for reproducible image analyses across experiments to reduce introduction of bias. By reducing workflow handoffs while preserving parameter control and exportable results, BactoMate enables accessible, reproducible, and scalable quantitative analysis of microbial microscopy data.

BackgroundThe quantification of fungal spores constitutes a fundamental metric in phytopathology, serving as the primary variable for inoculum standardization and being used as a proxy for disease severity. Historically, spore quantification has relied on manual hemocytometry, which remains the most precise counting process to date, where chambers such as the Malassez slide are used to count a subsample of the inoculum. However, this method applied manually is highly labor-intensive, time-consuming, and can be prone to operator-dependent variability. To overcome these limitations, we introduce MIRA (Microscopy Image Recognition & Analysis), a novel open-source software integrating You Only Look Once (YOLO) deep learning algorithms. Featuring a user-friendly graphical interface, MIRA is adaptable to multiple camera systems and supports advanced object detection models, including YOLOv11 and YOLOv26. ResultsWe demonstrate that MIRA can be used to accurately detect and count spores from several phytopathogenic fungi, automatically measure spore surface area, and to differentiate spores across different genera. In an exhaustive comparative analysis using Pyricularia oryzae spores as an example, MIRA was benchmarked against manual gold-standard counting slides (Malassez and Kova) and indirect spectrophotometric methods (SPARK). The P. oryzae model loaded via MIRA achieved a strong correlation (R = 0.96) with manual gold standards while reducing processing time by over 90% for high-concentration samples (10 spores/mL). Beyond this benchmark, we also successfully tested specific YOLO models designed to recognize macro- and microconidia of Fusarium oxysporum f. sp. cubense, a model for Pseudocercospora fijiensis, and a single multiclass model capable of identifying six different rice pathogenic fungi. We provide comprehensive tutorials for operating the software and training custom detection models for free using Roboflow and Google Colab. MIRA is available both as open-source Python code and as standalone executables for Windows and Linux. ConclusionsMIRA provides a rapid, accurate, and highly reproducible alternative to manual spore counting, effectively removing a major bottleneck in phytopathology workflows. By combining advanced YOLO-based deep learning with an accessible interface and comprehensive training resources, MIRA makes accessible automated image analysis for researchers without programming expertise. Moreover, MIRA drastically improves the efficiency of high-throughput disease phenotyping and can be adapted for a wide range of microscopic quantification tasks across various biological disciplines.

BackgroundDetection of rare mycobacterial genotypes, including those associated with antibiotic resistance or population heterogeneity is important for diagnostic, therapeutic and research applications. This depends on efficient recovery of genomic DNA (gDNA) from sampled populations, a challenging requirement in paucibacillary clinical materials. Mycobacteria have uniquely lipid-rich, structurally robust cell envelopes which resists cell lysis by conventional methods. Here, we characterize mycobacteriophage D29-mediated lysis at the single-cell level, evaluating its utility as a biological lysis strategy for mycobacterial DNA isolation, benchmarked against the standard cetyltrimethylammonium bromide (CTAB) extraction method. MethodsConditions for mycobacteriophage D29 infection of Mycobacterium smegmatis (Msm) were established, and single-cell phage adsorption and phage-mediated lysis visualized through live-cell time-lapse fluorescence microscopy (FM). A mycobacteriophage D29-based lysis method was applied to both Msm and M. tuberculosis (Mtb), and extraction efficiencies compared with the standard CTAB method. Cell lysis efficiency was quantified by colony forming units (CFU), flow cytometry (FC) and FM; DNA yield was determined by quantitative polymerase chain reaction (qPCR) and droplet digital PCR (ddPCR). ResultsMycobacteriophage D29 adsorption was observed at the poles and septa of individual mycobacterial cells. Phage infection was associated with loss of cytoplasmic green fluorescence protein (GFP) reporter protein, with uptake of a cell death marker propidium iodide (PI). Mycobacteriophage D29 infection resulted in a marked loss of cell viability, with >6log10 reduction in CFU, and cell lysis efficiencies calculated as 93.3% (FC) and 96.8% (FM). Molecular quantification (qPCR and ddPCR) indicated that the mycobacteriophage-based lysis achieved between 4- to 7-fold greater gDNA yields in Msm and between 3- to 12-fold greater gDNA yields in Mtb H37Ra compared with the CTAB method. Notably, gDNA extraction efficiencies in both mycobacterial species exceeded 92% in low-biomass samples containing approximately 100, 175 and 320 bacilli. ConclusionThese results demonstrate the utility of the mycobacteriophage D29-based method for improved DNA extraction yields from mycobacteria through direct lysis of individual bacilli, with performance suited to low-biomass samples. SummaryRecovering genomic DNA (gDNA) from low numbers of mycobacteria is a persistent bottleneck for diagnostics and genomic studies, because the lipid-rich mycobacterial envelope resists conventional lysis. Here we show that mycobacteriophage D29 provides an efficient, biologically selective route to mycobacterial DNA. Leveraging single-cell live imaging, we reveal that phage D29 adsorbs preferentially at the poles and septa of individual cells, and that infection is heterogeneous and asynchronous, progressing from envelope permeabilization to loss of viability. Applied as an extraction method and benchmarked against the standard cetyltrimethylammonium bromide (CTAB) protocol, phage D29-mediated lysis recovered 4- to 7-fold more gDNA in Mycobacterium smegmatis (Msm) and 3- to 12-fold more in Mycobacterium tuberculosis (Mtb). Critically, extraction efficiency exceeded 92% in both species in low-biomass samples of approximately 100, 175 and 320 bacilli, where CTAB performed poorly (<20% efficiency). These findings support phage-mediated lysis as a quantitative, near-complete DNA-recovery method that outperforms conventional extraction precisely in the paucibacillary regime of greatest clinical relevance and demonstrate the value of single-cell interrogations in building towards precision tools to engage the mycobacterial cell.

Viral diagnostics during early phases of infection are often limited by target scarcity and the deployment tempo. We significantly advance both quantitative accuracy and diagnostic throughput of viral agglutination assays with Immuno-Janus Particle (IJP) aggregation behavior that "flicker" stochastically with size-dependent statistics. By scrutinizing microscale blinking patterns of time series fluorescent videos, we decipher Brownian dynamics of individual IJP-Virus conjugates and IJP aggregates via windowed Ito stochastic analysis (termed the Culsans method). High-frequency rotational fluctuation is deconvolved from corrupting drifts caused by gravitational sedimentation and Brownian translational motion. This methodology enables a non-linear mapping of angular positions of detected IJPs and IJP aggregates to extract rotational diffusivity (Dr) (and subsequently overall construct size) with superior linearity (R2[&ge;]0.85). The aggregation behavior exhibits a maximum when the IJP and viral particle concentrations are equal. The virion-bridged IJP-IJP conjugates significantly shift the detectable hydrodynamic diameter in the Poisson limit of reduced virus concentration with respect to IJPs, pushing the limit of detection (LOD) to 103 - 104 virions per mL in untreated human plasma. This tunable platform offers a rapid, low-volume, and scalable alternative to lab-based RT-PCR, bridging the gap between virion sensitivity and field-readiness.

Plant growth-promoting bacterial endophytes represent a sustainable strategy for enhancing agricultural productivity while reducing reliance on synthetic fertilizers and pesticides. This study focused on the genomic and functional characterization of two endophytic bacterial strains, R11F and R19M, isolated from bean and maize roots, respectively. Comparative analyses based on 16S rRNA gene sequences, average nucleotide identity (ANI), and genome-to-genome distance calculations (GGDC) classified both isolates as Pseudomonas palleroniana. Comparative genomic analyses revealed highly conserved genomes containing genes associated with plant colonization, phosphate solubilization, stress adaptation, heavy metal resistance, and hydrocarbon degradation. Genome mining further identified 17 and 18 biosynthetic gene clusters (BGCs) in R11F and R19M, respectively, including non-ribosomal peptide synthetases (NRPS), pyoverdine, NRP-metallophores, RiPP-like compounds, arylpolyenes, {beta}-lactones, terpenes, NAGGN, and hydrogen cyanide. Strain-specific BGCs associated with syringomycin and viscosin biosynthesis were identified in R11F, whereas R19M harbored clusters related to asplenin and kolossin biosynthesis. In vitro assays confirmed indole production, phosphate solubilization, and siderophore production, as well as the ability of both strains to grow in nitrogen-free medium. Both strains significantly inhibited the growth of Fusarium oxysporum, Phytophthora cinnamomi, and Colletotrichum gloeosporioides. Furthermore, plant inoculation assays demonstrated host-dependent growth promotion, with R11F showing the most consistent improvements in plant growth parameters in tomato, wheat, and lentil. Overall, the integration of comparative genomics and experimental validation demonstrates that P. palleroniana R11F and R19M possess complementary traits associated with plant growth promotion, pathogen suppression, saline stress adaptation, and bioremediation.

Klebsiella pneumoniae is an important cause of severe respiratory and systemic infections, and the increasing prevalence of multidrug-resistant strains has created an urgent need for alternative antibacterial strategies. In this study, nine K. pneumoniae-infecting bacteriophages isolated from diverse environmental sources were characterized genomically and functionally. Genome analyses revealed substantial genomic and proteomic diversity among the isolates. Functional screening against the clinical K. pneumoniae isolate JJD85 identified Curly as the most active phage, producing the highest plaque-forming titer and rapid suppression of bacterial growth in liquid culture. Curly was predicted to have a virulent lifestyle and encoded structural, genome-packaging, and DNA replication-associated proteins. In primary human monocyte-derived macrophage cultures, Curly markedly reduced bacterial burden in both cell-associated and cell-free fractions, while treatment of primary human neutrophil cultures produced an approximately 10^6-fold reduction in total recoverable bacterial burden. Transmission electron microscopy demonstrated phage-like particles within bacterial profiles located in both extracellular and macrophage-associated intracellular compartments. In a C57BL/6J murine pneumonia model, intranasal Curly treatment reduced pulmonary bacterial burden in a dose-associated manner, with approximately 10-fold and 100-fold reductions at the low and high doses, respectively. Curly treatment also attenuated infection-associated lung inflammation and preserved pulmonary architecture. These findings identify Curly as a promising bacteriophage candidate against K. pneumoniae and support further evaluation of its host range, resistance profile, and therapeutic potential.

Microbial-mediated plastic degradation has the potential to address the persistent global problems of plastic waste and pollution. Previous work has shown that soils can harbour microbes capable of plastic degradation, but we expect there is a broader diversity of soil microbes capable of metabolizing plastics than identified to date using more traditional cultivation-based screening methods. Here we demonstrate a novel approach to identify putative plastic degrading microbes in soil. We paired in situ, real-time measurements of microbial plastic degradation on conductive sensors with subsequent microbial community profiling of the sensor-associated biofilms exhibiting appreciable degradation. To illustrate the utility of our approach, we focus on microbial degradation of the bioplastic polymer PHBV, poly(3-hydroxybutuyrate-co-3-hydroxyvalerate). We screened a range of soils with the in situ sensors to identify a subset of five soils with high PHBV degradation rates, and confirmed that PHBV degradation was due to microbial activity. We then extracted DNA directly from sensors placed in soils with high measured rates of PHBV degradation and used marker gene sequencing to identify the bacterial and fungal taxa associated with the observed PHBV degradation. We confirmed via in vitro culturing that microbes isolated from the sensors have a demonstrated capacity for PHBV metabolism. Together, these results highlight the benefit and feasibility of using low-cost, in-soil sensors to simultaneously collect real-time data on plastic degradation rates in soil and identify previously unrecognized microbial taxa capable of degrading and metabolizing plastic polymers in situ.

Plant cell walls are polysaccharide-rich extracellular matrices composed of multiple complex carbohydrate polymer networks, including cellulose, hemicelluloses, pectins, and glycosylated proteins. Polysaccharide deposition critically impacts cell wall structure, and structural microheterogeneity within cell wall glycans also influences polymer rigidity and polymer-polymer interactions. Collections of monoclonal antibodies (mAbs) have been developed to target unique carbohydrate epitopes within cell wall polysaccharides and to investigate how these structural changes impact cellular and plant development. Here, we implement generalizable methods to attach unique DNA barcodes to mAbs that recognize major cell wall polysaccharide classes. By applying these mAbs individually to polysaccharide standards, we demonstrate that bound DNA barcoded antibody abundance can be measured via quantitative PCR. Additionally, we demonstrate that DNA conjugated antibodies can be pooled to quantitatively analyze polysaccharide epitope composition of polysaccharide standards and fractionated cell wall material by amplifying their unique barcodes via qPCR. These results demonstrate that barcoded polysaccharide-directed mAbs offer sensitive, quantitative insights into cell wall polysaccharide composition and facilitate multiplexed profiling of cell wall polysaccharide abundance. This approach will also enable multiple future high-throughput applications, such as glycome profiling, spatial glycomics, and glycan interaction measurements, that will further our understanding of cell wall compositional impacts on plant physiology.

Fungal contamination poses a growing threat to spacecraft integrity, crew health, and planetary protection efforts. We describe a scalable and interpretable pipeline for identifying fungi with adaptation potential to spaceflight-associated stress conditions such as extreme temperatures, radiation levels, etc., and pathogenicity risks. Starting with proteins known to confer stress resistance, we identify orthologs across over fifteen hundred fungal species and evaluate their contamination potential via comparative proteome analysis. Our pipeline integrates proteins with known functional inference, cross-database proteome matching, and identity-based scoring to generate a ranked list of fungal species of concern. We apply this approach to detections from spacecraft assembly facilities, highlighting species with combined stress-tolerance and pathogenic potential. This study establishes a foundation for future AI-based risk assessments that can scale to orders of magnitude more fungal species, thus laying the foundation for systematic identification and assessment of fungal contaminants with potential adaptation and pathogenicity risks in spaceflight environments, thereby supporting contamination control strategies for future space missions. We also present an interactive visual online tool for researchers to trivially check the contamination potential of species in their own samples.

Fluorescence microscopy is central to the study of bacterial cell biology, multicellular behaviours, and host-pathogen interactions. Bright, robust and photostable labelling is required for bacterial identification, sorting and quantitative analysis, driving continuous development of state-of-the-art labelling tools. Here, we developed a multicolor fluorescent cell labelling toolkit for Gram-negative bacteria carrying the attTn7 site, using the opportunistic human pathogen Pseudomonas aeruginosa as a model. Cell labelling is achieved by constitutive chromosomal expression of genes encoding a choice of four novel fluorescent proteins, mNeonGreen, mJuniper, mLychee and mScarlet-I3, codon-optimised for P. aeruginosa. These reporters provide bright, stable fluorescence with minimal photobleaching and excellent spectral separation during long-term imaging of single cells, macrocolonies and biofilms. Chromosomal expression of mNeonGreen yielded brighter and more homogeneous labelling than expression of the same construct from a plasmid. Importantly, dual-color labelling of macrocolonies uncovered previously unrecognised phenomena of collective motility when two isogenic swarming populations interact. Finally, we demonstrate the applicability of our constructs in biologically relevant host-pathogen contexts by imaging both live and fixed P. aeruginosa-infected human airway epithelial cells. This versatile cell labelling platform enables reliable bacterial identification, segmentation, tracking, and quantitative fluorescence imaging across spatial and temporal scales, and is readily adaptable to most other Gram-negative bacteria as the attTn7 integration site is well conserved.

Archaea are promising chassis organisms in biotechnology, and the accurate annotation of their chromosomal replication origins (oriCs) is the key to unlocking their full potential. However, the existing Ori-Finder 2 web server suffers from low accuracy, slow speed, and limited scalability. In this study, we present Ori-Finder-Arch, an updated web server for high-performance oriC prediction in archaea. This pipeline integrates HMMER-based replication initiation protein (RIP) annotation, refined consensus motif recognition, and GC profile-based DNA unwinding element (DUE) detection. On a benchmark set of experimentally validated oriCs, Ori-Finder-Arch achieved a recall of 95.6% and a precision of 86.0%, substantially outperforming Ori-Finder 2 (62.2% and 63.6%, respectively), while running 4.75 times faster and supporting diverse assembly levels. When applied to the available archaeal assemblies, it successfully annotated 17,472 oriCs. Meanwhile, the web server provides interactive visualizations at different levels. In conclusion, Ori-Finder-Arch offers an efficient, accurate, and user-friendly platform for advanced studies of archaeal DNA replication initiation and synthetic biology applications, and is freely available at https://tubic.org/Ori-Finder-Arch/ and https://tubic.tju.edu.cn/Ori-Finder-Arch/.

Bacterial exopolysaccharides (EPS) are key components in biofilm formation, stress protection, and symbiosis in Rhizobiaceae. While EPS structural diversity is extensive, experimental characterization remains limited. In this study, we experimentally determined and compared four distinct EPS structures produced by ten Rhizobium alamii strains. Using genomic data, we bioinformatically identified supra-operonic clusters (SOCs) responsible for these EPS biosynthesis. We introduced a computational framework to predict, score, and compare EPS SOCs across 84 Rhizobium and Sinorhizobium species, linking gene content to structural and functional EPS diversity. A total of 743 EPS SOCs was selected for network analyses, allowing the identification of 36 major groups of orthologous EPS SOCs, successfully recovering all known EPS biosynthetic loci and two novels SOCs potentially encoding uncharacterized EPS (xEPS-I, xEPS-II). Profiles of EPS SOCs correlated with taxonomical groups, with a single EPS SOC conserved through all 84 genomes and distinct additional EPS SOCs depending on the group, but do not strictly explain symbiotic capacity. Genetic comparisons of transporters (Wzx, Wzy) and glycosyltransferase sequences indicated these proteins as key markers of EPS structure. Overall, this computational framework accurately identified and classified EPS SOCs, providing a scalable, genome-based method for predicting EPS biosynthetic potential in Rhizobiaceae and usable in other microbial genera.

Lysine acetylation is an evolutionarily conserved, post-translational modification that regulates metabolism and protein function, yet its role in archaeal electron transfer systems remains poorly understood. Here, we investigated lysine acetylation of the 2Fe-2S ferredoxin HvFdx (HVO_2995) and its flavin-dependent oxidoreductase HvFdR (HVO_2345) partner in the halophilic archaeon Haloferax volcanii. Genetic and biochemical analyses established HvFdx as an essential 2Fe-2S ferredoxin with a midpoint redox potential of -385 mV. Lysine acetylation of HvFdx was found to occur primarily at K119, a residue positioned near the [Fe-S] cluster interface, and to modulate electron transfer capacity without impacting Fe-S cluster incorporation, midpoint potential, or protein abundance. In contrast, HvFdR was found lysine acetylated at multiple sites in a manner consistent with a non-enzymatic mechanism that resulted in altered flavin binding, enzymatic activity, and thermal stability. Lysine acetylation of HvFdx was found to stimulate electron flow from HvFdR as measured by an anaerobic NADPH [-&gt;] HvFdR [-&gt;] HvFdx [-&gt;] DCIP assay. 3D structural modeling, proteomic, biochemical, and genetic assays suggest the haloarchaeal GNAT-family acetyltransferase homolog HVO_2874 as a candidate enzyme associated with HvFdx lysine acetylation and optimal growth of H. volcanii. Together, these findings demonstrate that lysine acetylation differentially regulates archaeal redox-active proteins and functions as an important mechanism coordinating redox metabolism in H. volcanii.

The perennial grass Miscanthus x giganteus (miscanthus) offers a sustainable alternative to traditional biomass feedstocks while improving key soil health parameters, including aggregation. Aggregate stability results from dynamic soil-plant-microbe interactions, yet the relative importance of each factor remains an active research question. Building on previous observations that miscanthus alters soil structure to improve water-holding capacity and aggregate stability, we characterized the communities of soil bacteria and arbuscular mycorrhizal fungi (AMF) across three sites in Iowa, USA, comparing miscanthus to annual maize (Zea mays L.) and non-cropped perennial turfgrass (Poa spp.). We examined whether microbiomes co-varied with soil aggregation and, if so, whether plant cover identity or life history categorization better explained the observed patterns. Bacterial and AMF communities varied across sites and plant types, with signals that life history and plant cover identity both mattered. Aggregate stability aligned with a perennial-annual divergence in microbial beta diversity, while finer-scale differences in community composition and network structure were plant-specific. Soils under perennial plants were enriched in microbial groups positively correlated with aggregate stability; we identified 61 bacterial and 8 AMF "architect" taxa for future study. Within- and cross-kingdom co-occurrence network analysis revealed greater complexity under perennial plants: 1.9-fold more network links in miscanthus bacteria-bacteria networks than in maize, and 1.7-fold more in turfgrass AMF-AMF networks. Miscanthus fundamentally shapes microbial interactions, particularly among bacteria, relating to improved soil physical structure. Understanding these soil-plant-microbe feedbacks advances the development of biomass feedstocks with a portfolio of soil health benefits for next-generation biofuels and bioproducts. IMPORTANCEPerennial bioenergy crops can provide the raw material for biofuels and bioproducts while simultaneously improving soil health. Miscanthus x giganteus (miscanthus) efficiently stabilizes soil aggregates, potentially leading to higher water retention and erosion resistance. Understanding the microbial contributions to these outcomes is key to building resilient, sustainable bioenergy systems. This study highlights the connections between communities of soil microbes--bacteria and arbuscular mycorrhizal fungi--across three sites and three plant covers, including miscanthus, maize, and turfgrass. We identify a guild of potential "microbial architects" linked to soil aggregation and show more interconnected microbial networks under the perennial plant covers compared to annual maize. These insights shed light on the interactions between soil biological communities and soil physical and chemical properties. More broadly, the results may inform efforts to harness plant-associated microbiomes for sustainable biomass production.