mLife

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.

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.

Biochar amendments are increasingly applied to improve soil physical functioning and support carbon dioxide removal, but their effects on intrinsic soil thermal properties remain poorly characterised. We conducted the first global systematic meta-analysis of 19 independent studies, 231 control-biochar comparisons, and 529 property-specific effect sizes to test how biochar changes soil heat transfer and storage. Biochar reduced thermal conductivity by 17.6% (95% CI, -22.7 to -12.2), thermal diffusivity by 11.0% (-14.5 to -7.3), and volumetric heat capacity by 8.3% (-12.3 to -4.1). Gravimetric heat capacity showed no significant overall response (+3.3%; -7.6 to 15.4) but was supported by fewer studies. Negative responses were directionally consistent for thermal conductivity, diffusivity, and volumetric heat capacity. Moderator analyses showed that responses were most consistently associated with post-application bulk density and changes in bulk density, while application rate modulated response magnitude and soil texture constrained context dependence. Co-variation among thermal conductivity, thermal diffusivity, and volumetric heat capacity matched expected physical dependencies, indicating coordinated structural reorganisation rather than independent shifts in isolated parameters. These estimates describe intrinsic conductive and storage properties; field-scale soil temperature responses may also be modified by albedo, evaporation, vegetation, and surface energy balance. Improved integration of soil thermal measurements with moisture dynamics, structural changes, and carbon cycling is essential to accurately represent biochar effects in soil and land-surface models.

Biological ethanol production is important for the circular carbon economy and makes up 73% of the U.S. biological fuels market. Previously, we produced ethanol by reversing methanogenesis and capturing methane by cloning methyl-coenzyme M reductase (Mcr) from an unculturable population of anaerobic methanotrophic archaea; this process was predicated on the generation of the intermediate acetate and its conversion by the methanogenic host to ethanol. Moreover, methanogens are generally thought to be detrimental for converting acetate to ethanol and are usually intentionally inhibited. Here, we demonstrate that direct growth on acetate as the sole carbon and energy source by the methanogen Methanosarcina acetivorans C2A results in 40% of the metabolized acetate becoming ethanol and that there is 430% more ethanol produced, compared to growth on methane via Mcr. In addition, we found growth on methanol results primarily in methane generation and low levels of ethanol. Therefore, acetate may be readily converted by the methanogen M. acetivorans to ethanol at high yields.

Oxygen consumption is a direct functional readout of bacterial respiration and metabolic state, yet existing methods for quantifying oxygen dynamics are limited in throughput and temporal resolution. Here, we establish a high-throughput platform for real-time profiling of bacterial respiration by adapting the Resipher, a non-invasive oxygen quantification system, for use in bacterial cultures. Measurements obtained with the Resipher were comparable to those generated using a Clark-type electrode-based high-resolution respirometer, validating its quantitative accuracy. Across Gram-negative (Escherichia coli, Francisella novicida) and Gram-positive (Enterococcus faecalis, Staphylococcus aureus) species, the Resipher generated reproducible measurements under both growth-permissive and growth-limited conditions, enabling assessment of respiration, independent of proliferation. Functional profiling revealed that oxygen consumption responds dynamically to nutrient availability and electron transport chain perturbation, including species-specific inhibition by benzarone. Notably, oxygen consumption profiles distinguished bactericidal and bacteriostatic antibiotics, with bactericidal agents transiently increasing respiration and bacteriostatic agents suppressing metabolic activity. Together, these findings establish oxygen consumption as a sensitive physiological readout and highlight the potential utility of respiratory profiling for mechanistic studies.

Phage therapy offers a promising solution to the antimicrobial resistance crisis. However, a major concern preventing the adoption of phage therapy is the potential for unintended consequences of phage release; both in regard to preventing the spread of phage resistance, and the proliferation of a non-endemic virus into the microbial ecosystem. Conditional replication (biocontainment) of phages through bioengineering may address these concerns, but the impact on bactericidal efficacy is unknown. Here, we created a biocontained T7 phage (T7{Delta}capsid) lacking the major structural capsid gene, gp10AB, that can only replicate on Escherichia coli strains expressing gp10AB in trans, and assessed its bactericidal efficacy compared with wild-type T7. Congruent with model predictions, T7{Delta}capsid was only able to clear a well-mixed culture of E. coli at a multiplicity of infection (MOI) of 10 or higher, whereas wild-type T7 prohibited growth at an MOI of 0.1. The reduction in efficacy was more evident in a complex structured environment within a microfluidic device, where phage success depends on its ability to penetrate a microbial niche via propagation. In this environment, T7{Delta}capsid was unable to propagate into the bacterial population and unlike wild-type T7, had no impact on the population's growth. This study shows that whilst biocontainment of phages may improve the biosafety of phage therapy, it comes at the cost of its propagation efficacy and niche penetration in relevant environments.

Plant growth-promoting bacteria are gaining significant attention as promising biofertilizers. However, the inconsistency between in vitro plant growth-promoting traits and actual field performance remains a challenge, driven partly by a limited understanding of in situ colonization. This study characterized the colonization patterns of Citrobacter sedlakii CESi7, a novel plant growth-promoting bacterium, isolated from oil palm waste compost, during Brassica rapa cultivation. The in situ behavior of CESi7 was observed in both sterilized medium and non-sterilized soil using fluorescence in situ hybridization with a strain-targeting probe. The results revealed that CESi7 can establish both epiphytic and endophytic populations that transiently colonize roots. In a sterilized medium, CESi7 was widely distributed throughout the root tissues. Conversely, in non-sterilized soil, the bacterium formed dense aggregates specifically at the root tips. This study provides direct microscopic evidence of the colonization strategy of CESi7, offering crucial insights for its development as an effective biofertilizer.

The basidiomycete Ustilago maydis is a well-characterized model organism for studying pathogen-host interactions and of great interest for a broad spectrum of biotechnological applications. We set here to develop light inducible molecular tools to enable dynamic studies on signaling networks and fungi-host communication, and for metabolic engineering approaches. In particular, light-controlled, optogenetic switches provide quantitative, spatio-temporal control capabilities, are minimal invasive and reversible. We engineered two blue light-inducible LOV-domain-based gene expression switches, to up- (Blue-ON) and down-regulate (Blue-OFF) gene expression, and performed a functional characterization in sporidia and hyphae of U. maydis. Profiting from the dynamic control ranges and rapid kinetics, we implemented the optoswitches to control cell morphology by initiating the transition from a haploid sporidial cellular morphotype to filaments upon regulation of the levels of the polarity factor Rac1 and its constitutive active mutant Q61L. In addition to showing how expression level of effectors can be precisely regulated as an approach to understand fungi-plants interaction, we show in two proof-of-principle applications targeted control over U. maydis filamentous fungal invasion of plant tissue and the mechanisms of tumor formation. For this we placed under Blue-ON and Blue-OFF control two U. maydis effectors, See1 (Seedling efficient effector 1) and TIN2 (Tumor inducing 2), and tumor formation was assayed on maize leaves. Taken together, this study established blue-light switches as effective tools to control morphogenesis and pathogenesis in U. maydis.

Most proteins targeted to the organelles of endosymbiotic origin are encoded in the nuclear genome, placing them under the regulatory dominance of the nucleus. For photosynthetic eukaryotes, nuclear-encoded chloroplast proteins arise via two routes: First, genes of cyanobacterial origin were relocated to the nucleus through endosymbiotic gene transfer (EGT). Second, proteins of eukaryotic origin emerged to support chloroplast function and structure. These proteins are reimported into the chloroplast via an import machinery. Reversing the transfer of such genes from the nucleus to the chloroplast genome may offer insights into chloroplast regulation and evolution. In this study, we established a highly efficient and accessible electroporation protocol for chloroplast transformation in the green alga Chlamydomonas reinhardtii, and used it to reverse-transfer two nuclear-encoded genes encoding proteins arising via the two routes described above: the cyanobacteria-derived chloroplast division protein FtsZ1 and the Rubisco-linker EPYC1 of eukaryotic origin. Regardless of origin, both chloroplast-encoded FtsZ1 and EPYC1 showed proper localization and functionality comparable to their nuclear-encoded counterparts. Together, our study provides a robust protocol for chloroplast transformation, a platform for investigating the evolutionary drivers of EGT, and a foundation for advancing chloroplast bioengineering. SIGNIFICANCE STATEMENTO_LIEndosymbiotic gene transfer has resulted in the mass migration of genes from the chloroplast genome to the nuclear genome. Reversing the gene transfer could reveal the evolutionary significance of genome partitioning. C_LIO_LIUsing the green alga Chlamydomonas reinhardtii, this study developed an efficient, electroporation-based protocol for chloroplast transformation. Relocating the genes encoding two chloroplast-targeted proteins, FTSZ1 and EPYC1, to the chloroplast genome showed that the proteins maintained normal localization and function. C_LIO_LIThe established transformation protocol facilitates systematic testing of reverse gene transfer to elucidate the potential evolutionary advantages of genome partitioning and opens new avenues for chloroplast bioengineering. C_LI

Virophages are double-stranded DNA viruses that hyperparasitize giant viruses infecting unicellular eukaryotes. Parasitization by virophages often reduces the replication of giant viruses, thereby modulating microbial communities in the environment. However, the molecular mechanisms underlying the tripartite relationship are largely unknown due to methodological limitations. In the present study, we developed a reverse-genetics system for a Sputnik virophage that parasitizes the amoeba-infecting giant virus, mimivirus. We demonstrated that transfection of genomic DNA could recover infectious virophage particles. Transfection of genomic DNA synthesized by circular polymerase extension reaction (CPER) also resulted in the recovery of infectious viruses. As a proof of concept, we successfully modified two Sputnik genes by transfecting CPER-assembled mutant genomic DNA. Collectively, our reverse-genetics system provides a framework for assessing the functional importance of Sputnik genes and should facilitate future genetic studies of virophages. Significance statementVirophages are viruses that hyperparasitize giant viruses, which infect unicellular eukaryotes and have extremely large particles and genomes. Giant viruses modulate microbial communities not only by killing their hosts but also by altering host cellular functions. Virophages modulate the replication of giant viruses, thereby driving ecosystem dynamics. Previous studies have demonstrated their widespread distribution through isolation and metagenomic analyses. However, the functions of most virophage genes remain unknown. Due to the lack of genetic tools, the molecular mechanisms underlying the interactions between virophages and giant viruses remain largely elusive. Here, we established a virophage reverse-genetics system based on circular polymerase extension reaction. Our results demonstrate that the system can dissect virophage gene functions and will accelerate virophage genetics.

Chronic infections persist in large part thanks to protection that biofilms afford their bacterial creators. The extracellular polymeric substance of biofilms is a hydrated matrix of DNA, polysaccharides, and structural proteins, amongst other components, through which nutrients, signalling molecules, and antimicrobial agents must diffuse to reach the bacteria within. Quantitative measurement of transport on the nanoscale within in vivo biofilms remains challenging due to optical heterogeneity, autofluorescence, active remodelling of biofilms and the ambiguity in trajectory reconstruction during single-particle tracking (SPT). Here, we present a methodological framework for measuring molecular transport in defined minimal extracellular matrix models using quantum dots as fluorescent nanoscale probes imaged with high-speed SlimVar microscopy. To establish conditions in which high-diffusivity particle trajectories can be reliably reconstructed, upper limits to quantum dot concentrations were estimated from Brownian motion. The 99th-percentile inter-frame jump distance was estimated from the three-dimensional Brownian jump distance distribution and used to define a target average nearest neighbour distance, and therefore a per-particle volume, used for calculating a concentration which minimises the probability of trajectory collision during data acquisition. Quantum dot movement was imaged at sub-millisecond frame rates and diffusion coefficients were calculated in a 20% glycerol control and in DNA nanostar hydrogels modelling minimal extracellular matrix scaffolds assembled at 250 M and 500 M. Median diffusion coefficients decreased from 94.9 m2*s-1 in glycerol to 15.9 m2*s-1 and 8.3 m2*s-1 in the 250 M and 500 M hydrogels, respectively. More broadly, this work establishes a workflow for quantitative SPT in minimal biofilm models. Rather than attempting to reproduce the full biological complexity of native biofilms, this approach provides the basis of a modular experimental framework in which individual extracellular matrix components can be incorporated sequentially and their effects on molecular transport quantified.

This study evaluates the novel bacteriophage UCB24 as an eco-friendly biocontrol agent against Erwinia amylovora, the bacterial pathogen responsible for fire blight. Following purification, UCB24 was characterized for its optimal multiplicity of infection (MOI), infection kinetics, and environmental stability across diverse pH and temperature ranges. Microtitration assays and scanning electron microscopy (SEM) confirmed distinct morphology of the phage and its potent capacity to disrupt E. amylovora biofilms. Whole-genome sequencing and phylogenetic profiling identified UCB24 as a genetically distinct relative of four known phages. Furthermore, protein-protein interaction analyses revealed a strong binding affinity between the phage lysin and the hosts N-acetylmuramic-acid 6-phosphate etherase, uncovering the precise molecular mechanism driving targeted host destruction. In vivo plant trials demonstrated exceptional protective efficacy in Apple cv. Gala (88.61%) and Quince cv. Esme (81.37%), significantly outperforming traditional copper treatments under severe baseline pathogen pressure. Consequently, UCB24 represents a highly effective and sustainable biopesticide for managing fire blight in susceptible orchard ecosystems.

Extremophilic red microalgae are promising platforms for sustainable biotechnology, combining robust growth under selective thermoacidophilic conditions with production of thermostable phycobiliproteins and carbon-rich biomass. However, reactor-dependent effects on growth, product formation and biomass composition remain insufficiently resolved. Here, we systematically evaluated the extremophilic red microalga Cyanidioschyzon merolae across cultivation scales and reactor formats and benchmarked its performance against the well-established Galdieria javensis and Limnospira platensis. In small-scale multi-cultivator photobioreactors and microfluidic growth chambers, C. merolae showed superior growth, reaching a maximum growth rate of 0.034 {+/-} 0.001 h-1 and 8.3 {+/-} 0.3 g l-1 cell dry weight. Microfluidic cultivation enabled growth analysis at single-cell resolution and matched growth rates obtained in photobioreactors. To identify scalable production strategies, C. merolae was further cultivated in a flat-panel photobioreactor and a custom-designed internally illuminated photobioreactor. The custom-designed photobioreactor delivered the highest biomass concentration and productivity, yielding 11.5 {+/-} 0.6 g l-1 cell dry weight and 1.07 {+/-} 0.06 g l-1 d-1, and comparable yields with regard to R-phycocyanin and R-allophycocyanin. Biomass analysis revealed substantial carbon and nitrogen contents, starch accumulation up to > 20 % of cell dry weight, and fatty acids dominated by palmitic, linoleic and oleic acids. Despite its reduced cell wall fraction, C. merolae contained structurally diverse, cultivation-dependent polysaccharides. These results establish C. merolae as a versatile chassis for thermostable pigment production and renewable feedstock generation, highlighting photobioreactor design as a key determinant of productivity and biomass quality.

Biological control strategies are increasingly being explored as sustainable alternatives for managing rice blast disease caused by Magnaporthe oryzae. In this study, we characterized three Bacillus pumilus isolates (DC01, DC09, and DC13) and evaluated their antifungal and plant-beneficial properties against M. oryzae. Whole genome sequencing revealed multiple biosynthetic gene clusters associated with the production of antimicrobial metabolites. All three isolates inhibited fungal growth in dual-culture assays, whereas heat-stable diffusible antifungal activity was primarily associated with the cell-free supernatants of DC09 and DC13. Exposure to bacterial supernatants disrupted fungal development, inducing abnormal hyphal morphology characterized by bulbous swelling, altered polarity, and increased branching in M. oryzae. Volatile organic compound assays further revealed that the DC isolates suppress fungal growth in the absence of physical contact. The isolates additionally inhibited the growth of other phytopathogenic fungi and selected human bacterial pathogens. All strains exhibited plant growth-promoting traits, including indole-3-acetic acid production and osmotic stress tolerance, whereas DC09 also displayed phosphate-solubilizing activity. Importantly, root inoculation with the DC isolates significantly reduced rice blast disease severity and induced expression of defense-associated genes involved in jasmonic acid/ethylene signaling and immune priming. Collectively, these findings identify the DC isolates, particularly DC09 and DC13, as promising multi-mechanistic biological control agents for sustainable rice blast management.

Capabilities for quantitative monitoring of chronic wounds remain an unmet clinical need, as existing diagnostic approaches rely on semiquantitative evaluation of symptoms that lack sensitivity especially during early stages of infection. Here we present a scheme for tracking wound physiology that leverages a miniature, wireless skin-interfaced device for non-contact, transient measurements of the flux of volatile organic compounds (VOCs) and water vapor from the wound microenvironment. Unlike emerging smart bandage platforms that rely on physical contact with the fragile wound bed to interrogate liquid-phase biomarkers, this strategy uses an engineered microclimate and suspended suite of sensors to measure the diffusive transport of wound-derived gases across the wound surface but separated from it. The result enables quantitative evaluation of metabolic activity and healing progression without perturbing the healing tissues. In biofilm growth models of Staphylococcus aureus, measurements demonstrate that trends in VOC flux correlate strongly with bacterial growth kinetics and precede any visible biofilm formation. Longitudinal monitoring in infected murine wound healing models shows that concurrent measurements of water vapor and VOC flux provide complementary physiological insights, capturing both the trajectory of barrier restoration and the dynamics of bacterial burden. The findings establish this non-contact sensing scheme as a distinct and clinically translatable paradigm for wound monitoring, with broad implications for non-invasive surveillance of disease states in which tissue metabolic activity and skin barrier integrity serve as actionable physiological readouts. Significance StatementLimited capabilities in continuous, quantitative assessment of a wound make early diagnosis and effective management challenging, particularly in cases of infection that rapidly progress before symptoms appear. Non-contact approaches for wound monitoring that preserve fragile tissue can transform wound care. In this context, gaseous flux from the wound bed provides an integrative measure of microbial activity and barrier restoration. This study establishes a wearable sensing platform that quantifies these fluxes in real time, enabling early infection detection and temporal tracking of wound healing. These results highlight a path toward personalized treatment strategies and reduced reliance on episodic clinical evaluation.

ObjectivesModel species are essential for fundamental research in biology. While a complete genomic sequence is a prerequisite for genetic studies, it is not enough on its own. Understanding the three-dimensional organization of the genome is also important, allowing researchers to gain a more realistic understanding of the mechanisms governing genome function. In the fungal model Podospora anserina, although the genomic sequence has been established for a long time, the three-dimensional organization remained unknown. Here we obtained the first Hi-C datasets and present associated 3D models, providing the research community with a valuable resource for better multi-omics data integration. Data descriptionHi-C experiments were performed in duplicate, using nuclei purified from wild-type fungal mycelium. Four FASTQ files were obtained (two per replicate) and used as inputs for the 3DGB workflow with four different output resolutions, to observe the genome organization of P. anserina at different levels of detail (50 kb, 20 kb, 10 kb, and 5 kb). In a context where researchers already have, for this species, a large amount of traditional omics data (ChIP-seq, RNA-seq, etc.), these 3D models are helpful for complementing the linear representation of the genome, which is traditionally used in bioinformatic analyses.

Multidrug-resistant pathogens pose a major threat to One Health. Within the past decade, CRISPR-Cas systems have been explored as sequence-specific antimicrobials. While chromosomal injury has been considered the primary mechanism underlying pathogen killing by CRISPR-Cas antimicrobials, the synergistic role of gene disruption together with chromosomal injuries remains poorly understood. In this study, we characterized a new class of CRISPR-Cas antimicrobials that simultaneously cotarget essential and defensive genes to enhance potency against the clinically relevant pathogen Staphylococcus aureus. High-throughput CRISPR screening identified top-performing guide RNAs for twenty functionally diverse essential and defensive genes across the S. aureus genome. CRISPR-Cas antimicrobials were modularly formulated to target single or multiple gene loci and packaged in phage-like particles for specific delivery. By engineering an S. aureus production host with a chromosomally integrated anti-CRISPR protein, we demonstrated efficient production of CRISPR-Cas antimicrobials targeting any S. aureus chromosomal locus without self-targeting. Characterization of CRISPR-Cas antimicrobials with single guide RNA designs revealed that potency varied according to targeted gene function, achieving up to a 4-log10 reduction in viability and outperforming traditional antibiotics. Multiplexed configurations were consistently more effective than single-targeting designs, with the top-performing design demonstrating a 4.7-log10 reduction in viability. Cotargeting essential and defensive genes revealed synergies that led to improved lethality and attenuated resistance, with enhanced activity in biofilms compared to traditional antibiotics. Genes involved in signaling and stress responses were important defensive targets for developing cotargeting CRISPR-Cas antimicrobials. Overall, this study establishes design principles for synergistic CRISPR-Cas antimicrobials applicable to next-generation precision antimicrobial development. SIGNIFICANCEThe ability to effectively combat multidrug-resistant pathogens is of primary importance to One Health. This study develops a generalizable design principle for formulating potent CRISPR-Cas antimicrobials that exploit synergistic cotargeting strategies for enhanced pathogen killing. In addition to chromosomal injuries, we found that disruption of gene function plays a crucial role in determining the lethality of CRISPR-Cas antimicrobials, providing a generalizable framework for effective CRISPR-Cas antimicrobial design. The development of a CRISPR-Cas antimicrobial production host with stable, chromosomally integrated anti-CRISPR genes greatly expands the modularity, adaptability, and efficiency of formulating CRISPR-Cas antimicrobials and enables deeper insights into the molecular mechanisms involved in eliminating multidrug-resistant pathogens.

The architecture of Bacillus subtilis biofilms is influenced by the coordinated regulation of cellular specialization, matrix assembly, and metabolism. B. subtilis can form different types of biofilm in diverse physical and chemical environments. Understanding the molecular mechanisms that drive biofilm heterogeneity and adaptation to different environmental niches is crucial for developing more effective strategies to control their formation. In this study, we developed a tightly dual-regulated CRISPR interference (CRISPRi) system and employed multi-scale imaging to investigate the functions of individual genes in two distinct biofilm models: the floating pellicle and the intricate, three-dimensionally structured macrocolony, which develop at the liquid-air and solid-air interfaces, respectively. Our findings validated the CRISPRi approach as a powerful method for studying biofilm development over extended periods and revealed that numerous small non-coding RNAs are involved in regulating biofilm growth dynamics and architecture. The CRISPRi approach was also applied to a pool of 507 genes and transcription units, including protein-coding genes and non-coding RNAs, to screen for cell fitness in these two biofilm models. We discovered that, while both biofilm forms rely on fundamental processes such as cell wall synthesis and nucleotide metabolism, they exhibit different genetic dependencies with regard to matrix composition, motility, and signaling. Exopolysaccharide production, motility, and chemotaxis are crucial for pellicle formation. In contrast, macrocolony development is influenced by {gamma}-polyglutamate synthesis and nutrient acquisition functions. Genes of unknown function were also identified to play a differentially important role in the two biofilm forms. Additionally, the CRISPRi screens revealed further non-coding RNAs regulating biofilm architecture and growth dynamics, adding to the existing layers of post-transcriptional control. Collectively, these results demonstrate that biofilm formation at different physical interfaces is governed by a combination of shared and unique genetic pathways tailored to the specific biofilm environment, thereby opening research avenues into the molecular mechanisms specific to the solid-air and liquid-air interfaces.

The rapid advancement of high-throughput sequencing technologies has vastly increased the number of known protein sequences, but the experimental characterization of their structures and functions lags behind. This gap in knowledge impedes our understanding of biological mechanisms of these proteins, hinders the interpretation of high-throughput experiments, and exposes a significant challenge in modern biology: deducing the structural and functional information of proteins based on their sequences. Most computational approaches rely on homology with well-annotated proteins, yet many proteins lack identifiable homologues, reducing the power of this approach. Here, we integrated cutting-edge protein structure and function prediction methods to develop a complete sequence-structure-function pipeline that predicts structures and functions based on primary sequences. We applied this pipeline to predict the structure and function of all proteins in Escherichia coli UTI89, a model strain of uropathogenic E. coli. Based on the predicted functions, we performed enrichment analysis on the whole genome and revealed the possible roles and related biological mechanisms of poorly annotated proteins in this organism. Moreover, the performance of our pipeline was further validated through detailed case studies of the UTI89_C0931 and ybtS genes. Finally, we compiled the UTI89 structure and function database (https://seq2fun.dcmb.med.umich.edu/UTI89), offering it as a community resource to aid researchers in elucidating the roles of unannotated proteins in uropathogenic E. coli. This database aims to bridge critical knowledge gaps in microbial pathogenicity and resistance, enhancing our capacity to tackle emerging health threats.

Rapid and accurate pathogen identification is crucial for the clinical management of infectious diseases, particularly sepsis and severe respiratory infections, yet standard clinical workflows remain slow and resource-intensive. Here, we developed an automated, high-throughput imaging platform built on standard, clinically accessible bright-field microscopy, and generated a large dataset comprising 24.9 million label-free bacterial cells across six focal pathogens. Leveraging this resource, we trained a neural network (ESKAPe-ResNet) to identify ESKAPe species at the single-bacterium level. The model achieved >92% accuracy in species-level classification and >82% accuracy in quantifying ESKAPe abundance in mock mixtures, with high specificity against non-ESKAPe bacteria. In clinical validation using sputum, bronchoalveolar lavage fluid and blood samples from patients with respiratory infections and sepsis, the approach correctly identified the dominant ESKAPe pathogen in >78% of samples after minimum broth culture enrichment. The imaging-to-identification pipeline was completed in under 10 minutes, and coupled with brief cultivation, the median time to accurate identification was reduced to 5-6 hours, compared with days for conventional blood culture-based workflows. This work establishes the proof-of-principle for label-free, hardware-minimal rapid pathogen identification, providing a clinically deployable workflow to expedite diagnosis and reduce mortality in severe bacterial infections.