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Photosynthetic hydrogen (photoH2) production by the cyanobacterium Synechocystis sp. PCC 6803 is an attractive means for storing solar energy. However, photoH2 yields remain limited by competing electron flux pathways. Recent in vitro characterization suggests that photoH2 production requires electrons from both carbohydrate oxidation and photosynthesis. Engineered fusions between photosystem I (PSI) and hydrogenase (PSI-H2ase) aim to divert electrons toward H2 production and rely exclusively on photosynthesis. Thus, photoH2 production differs fundamentally between wildtype (WT) and PSI-H2ase fusion mutants. Here, we show that photoH2 production in WT is enhanced by supplemented glucose, consistent with the recently reported confurcating nature of HoxEFUYH H2ases. PhotoH2 production was further studied in the new psaE-hoxUYH mutant by simultaneously monitoring electron flux through PSI alongside with turnover rates of O2, CO2 and H2. PsaE-hoxUYH achieved the highest photoH2 yield and longest production period among the currently available PSI-H2ase mutants in Synechocystis, prolonged by removing O2. Upon illumination, psaE-hoxUYH exhibited high initial photoH2 production rates, which decreased in parallel with CO2 fixation and ceased immediately in the presence of O2. In absence of O2, photoH2 production still declined slowly. Therefore, in addition to CO2 fixation and O2, other yet unknown factors might limit photoH2 production under these conditions. Moreover, we traced a previously observed high H2 production phase of unclear origin in psaD-hoxYH cultures to contaminating [FeFe]-H2ases from Clostridium intestinale rather than genuine photoH2 production by the mutant. Together, these findings indicate a complex metabolic interplay tuning photoH2 production in Synechocystis WT and PSI-H2ase fusion mutants.

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.

The filamentous cyanobacterium Nostoc punctiforme is a key model organism used to study several aspects of cyanobacterial biology, including development, nitrogen-fixing symbioses with plants, and secondary metabolites, among others. While N. punctiforme is amenable to genetic manipulation, traditional approaches for the generation of mutant strains using homologous recombination are slow, requiring prolonged outgrowth under antibiotic selection to ensure isogenic mutant populations. CRISPR-based genome editing using Cpf1 (Cas12a) was recently shown to be an effective means of rapid generation of isogenic mutants in several cyanobacteria. In this study, Cpf1-based genome editing tools were developed for N. punctiforme. A total of 19 unmarked, in-frame deletion mutants were successfully constructed using Cpf1-targeted cleavage along with homology directed repair (HDR). The length of the homology arms (HAs) on the homologous repair template (HRT) used for HDR was found to be a critical factor for successful deletion of target genes, with some requiring up to 4 kb HAs to acquire mutant exconjugants. A strategy for allelic replacement was also developed by introducing an exogenous target site in place of the deleted genes, which could subsequently be targeted for cleavage and repaired with an HRT containing altered alleles of the genes of interest. Additionally, a single-step cloning strategy was devised, allowing for rapid assembly of editing plasmids, and improved conjugation protocols for genetic transfer from E. coli to N. punctiforme were implemented. Collectively, these tools and protocols should enhance the pace and ease of conducting genetic studies in this important model cyanobacterium.

We developed a convolutional neural network-based machine learning technique to simultaneously analyze the morphology and motility of spirochetal bacteria swimming with continuous cellular deformation. Matching probabilities between experimental images and learned models realizes quantification of cell morphology and association with motility. This method can be applied to diverse transformable cells, offering critical biophysical insights into microbial dynamics.

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.

Multispectral imaging is a cornerstone of chemical imaging, with bioluminescence, fluorescence, and phosphorescence imaging underpin much of preclinical and analytical chemical measurements. However, conventional implementations are typically proprietary, costly, and resolve spectral content from the reflectance of a broadband source, without control over the excitation spectrum. This architecture cannot isolate excitation-dependent photophysical processes. Spectral discrimination through sequentially resolved narrowband excitation, detected on a single broadband-sensitive camera, instead enables excitation-dependent fluorescence, phosphorescence, and reflectance measurements not available to such broadband approaches. We describe AURORA-MSI, an open-source multispectral platform that inverts this arrangement with fourteen narrowband LEDs spanning 367-940 nm, a 120-element annular illumination ring with eight independently addressable azimuthal sectors, and a thermoelectrically cooled monochrome CMOS camera. Radiometric calibration, spatial uniformity mapping, and camera noise characterization establish the quantitative measurement foundation. In bioluminescence imaging, AURORA-MSI localized sources in a calibrated tissue-mimicking mouse phantom comparably to a commercial Revvity IVIS Spectrum, detected luciferase-expressing HSJD-GBM1-001 glioblastoma cells across a dilution series, with reduced replicate consistency at the lowest densities, and mapped substrate-free fungal-pathway emission in an intact bioluminescent Petunia hybrida, none requiring photon-counting instrumentation. Time-resolved phosphorescence decay imaging of six inorganic phosphors over 33 minutes found tri-exponential models adequate at early times, while distributed-lifetime models were preferred at extended timescales. Multispectral image-texture features extracted across all fourteen excitation bands discriminated ten pharmaceutical and household powders spanning distinct chemical compositions and, in two cases, distinct formulations of the same compound. Generality beyond these regimes was established through dual-excitation fluorescence fingerprinting of fifteen mineral specimens and wavelength-selective plant-tissue imaging exploiting UV and NIR penetration-depth differences. Excitation-side spectral encoding enables photophysical measurements that detection-side systems with uncontrolled broadband illumination cannot isolate, and the cooled-camera architecture supports weak-signal modalities without photon-counting instrumentation.

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.

O_LIDrought and warming can disrupt soil microbial processes and ecosystem functioning. Although soil microorganisms can exhibit physiological adjustments to drought, it remains unclear how they allocate resources between extracellular resource acquisition, potential oxidative metabolism, and carbon storage during drought and recovery, particularly under constant warming and/or heat waves. C_LIO_LIHere, we tested the effects of drought on microbial resource allocation strategies across warming regimes during the resistance and recovery phases. We performed a full-factorial outdoor mesocosm experiment combining drought with constant warming and periodic heat waves, applied individually and in combination. We measured the potential activities of extracellular enzymes as proxy for the acquisition of microbial resources, the activity of dehydrogenase as a proxy for the potential active oxidative metabolism, and microbial glycogen pools as a proxy for carbon storage. We also quantified drought legacy effects by measuring microbial functioning before the new drought treatments, capturing the influence of the drought imposed in the previous year. C_LIO_LIDuring the resistance phase, dehydrogenase activity and glycogen pools remained stable, despite reduced extracellular enzyme production, while enzyme allocation shifted towards oxidative enzymes associated with acquisition of recalcitrant C in warming regimes. One month after rewetting, all microbial proxies no longer differed from the control soil moisture conditions. Drought legacy effects were observed in extracellular enzymes, dehydrogenase activity, and glycogen pools, with glycogen exhibiting the strongest legacy effect. C_LIO_LIWe conclude that the asymmetrical responses of extracellular resource acquisition and internal C storage to drought and warming may function as strategies for microbial survival in increasingly variable climates. C_LI

Spacer efficacy generally declines with distance from the leader sequence, but the scarcity of fine-scale studies hampers comparisons across taxa. Here, we investigated positional effects across an exceptionally long 121-spacer CRISPR array associated with the type I-C cas operon of a Myxococcus xanthus natural isolate. In plasmid-interference assays, we found that interference rapidly declined with distance from the leader sequence, with only the proximal ~4% of spacers conferring measurable interference. This contrasts strikingly with a study in Vibrio cholerae, in which it was shown that ~95% of spacers in a shorter (39-spacer) array were effective. Our results suggest that there is great variation in the effective proportion of spacers across species, highlighting the need for fine-scale studies of CRISPR-array activity across diverse bacterial lineages.

Bloom-forming cyanobacteria thrive in highly dynamic light environments, yet the mechanisms enabling rapid acclimation to fluctuating irradiance remain poorly understood. Here, we compared light acclimation in the bloom-forming cyanobacterium Microcystis aeruginosa PCC 7806 and the non-bloom-forming model cyanobacterium Synechocystis sp. PCC 6803 and investigated the role of the cyanobacterial toxin microcystin (MC) and its in vivo binding partner RubisCO in this process. Whereas Synechocystis grew faster under sustained high light, Microcystis performed better under low light and responded to transient high-light exposure with a remarkably rapid increase in photosynthetic activity and glycogen accumulation. These responses were markedly attenuated in an MC-deficient mutant. Although RubisCO from Microcystis exhibited pronounced light-dependent changes in activity, MC had only minor effects on RubisCO catalysis, arguing against a direct role in regulating enzyme function. Instead, extracellular MC elicited a transient transcriptional program characterized by induction of inorganic carbon acquisition systems, including the high-affinity bicarbonate transporter BCT1, consistent with activation of the carbon-concentrating mechanism (CCM) and enhanced carbon fixation in vivo. MC further stimulated the expression of photosynthesis-related genes, and altered carboxysome organization, and promoted extracarboxysomal localization of RubisCO. Together, our findings identify MC as a light-responsive signaling molecule that coordinates CCM activity, carbon acquisition, and photosynthetic acclimation, thereby enhancing adaptation of Microcystis to fluctuating irradiance and potentially contributing to its ecological success in cyanobacterial blooms.

With the rapid expansion of global food demand, aquaculture has become a critical pillar for future food security. However, aquaculture systems remain highly vulnerable to pathogenic bacteria, and rapid identification of antagonistic microbes is essential for sustainable disease control. Conventional evaluation approaches rely on fluorescence labeling or post-culture assays, limiting the ability to quantify dynamic interactions in mixed microbial populations in a real-time and label-free manner. Here, we propose a computational framework for classifying the mixing ratio of Vibrio harveyi and environmental bacteria using time-series motion features extracted from microscopy videos. We defined 24 interpretable motility descriptors and employed a Temporal Convolutional Network (TCN) to learn their temporal structure. The proposed method achieved a classification accuracy of 93.3%, outperforming conventional static statistical approaches and alternative machine learning models. These findings indicate that mixture discrimination in microbial communities is governed not by absolute motility magnitude, but by collective alignment and its temporal stability. Our study establishes a time-resolved computational framework for quantifying dynamic collective order in mixed microbial populations and highlights its potential for label-free automated screening and robotic microbiological applications.

Members of the amoebozoan genus Acanthamoeba are unicellular heterotrophic protists that inhabit a wide range of terrestrial and aquatic environments. They are opportunistic pathogens known to host disease-causing bacteria as well large DNA viruses. While Acanthamoeba is commonly used to study cellular locomotion, phagocytosis, and disease, genetic tools for precise editing of the Acanthamoeba genome are still in their infancy. Here we have developed CRISPR/Cas9 tools for genetically engineering the model acanthamoebid Acanthamoeba castellanii strain Neff. In proof-of-principle experiments, we targeted the myosin-II heavy chain gene. Evidence for editing was found using reverse transcriptase PCR, microscopy, and DNA sequencing, which also identified unique alleles for the target locus. Collectively, we demonstrate that CRISPR can be employed to edit Acanthamoeba genes using a knock-in approach. This work serves as a foundation to further develop A. castellanii as a model system with which to study diverse questions in cell and molecular biology, biochemistry and evolution.

Excess light absorption causes severe photo-oxidative damage and limits photosynthetic efficiency. Enhancing high-light (HL) tolerance and energy utilization in photosynthetic cyanobacteria holds great potential for sustainable biomanufacturing. Here, using Synechocystis sp. PCC 6803 (Syn6803) as a model, we generated eight independently evolved strains tolerant to hyper-HL (2000 mol photons/m2/s) through approximately two years of natural adaptive laboratory evolution (ALE). Remarkably, these strains circumvented common evolutionary trade-offs; all demonstrated markedly enhanced quantum yields and photosynthetic performance, with four strains exhibiting a 121.71%-168.36% increase in biomass accumulation under HL. When equipped with the heterologous sucrose transporter CscB, sucrose productivity under HL increased by up to 208.33%. Whole-genome resequencing unveiled a total of 77 mutations across the eight lineages, uncovering a diverse polygenic landscape for HL adaptation. Reverse genetics combined with structural modeling highlighted pivotal roles for regulatory and structural loci, including slr0758 (KaiC1) and slr1513 (SbtB), in modulating circadian rhythmicity and inorganic carbon uptake to prevent photo-oxidative overload. Overall, our findings uncover diverse evolutionary routes toward high-light acclimation and provide superior cyanobacterial chassis for photosynthetic biotechnology.

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.