Biology subjects

Arkin, A. P.

Publications and source records attributed to Arkin, A. P..

At least 19 recordsLinked to original sources

bioRxiv2026

Genome-wide characterization of host factors involved in single-stranded RNA and DNA phage infection pathways

Single stranded RNA (ssRNA) and single stranded DNA (ssDNA) bacteriophages represent a key component of the global virome, yet the host genetic networks supporting their infection cycles remain poorly understood. Here, we present a comprehensive, genome-wide mapping of the genetic landscape regulating infection cycles for F pilus-dependent ssRNA and ssDNA phages in Escherichia coli. Genetic screens across ssRNA phages spanning all four genogroups of the Leviviricetes revealed a highly conserved network of host dependencies, with the notable exception of the F plasmid gene traD. While primary structural receptor components and dsbA mediated disulfide bond formation are universally required across all lineages to ensure F pilus integrity, traD exhibits a strict genogroup-specific requirement during entry, showing variable essentiality across different viral groups despite sharing an identical primary receptor. Our gene dosage screens revealed that an elevated copy number of the hslU protease or the RNA chaperone stpA restricts infection, identifying clear genetic barriers that can perturb the viral life cycle. Parallel assays with filamentous ssDNA phages produced host factor profiles consistent with published literature, while revealing additional variations in host dependency. These screens confirmed that ssDNA phages strictly rely on the host TolQRA complex for entry downstream of pilus engagement. The assays tracked prominent negative fitness signatures across homeostatic clusters, highlighting how the physiological burden of continuous, non-lytic virion extrusion strains the host envelope. Finally, this comparative approach traced the selectivity of our isolation host (E. coli HSF) to a horizontally acquired capsule architecture from Klebsiella. This surface shield excludes a large panel of double stranded DNA phages isolated on diverse E. coli strains, while allowing virions from ssDNA and ssRNA phages to engage the extended F pilus and bypass the barrier via native pilus retraction. Together, this work provides a systematic, class-wide map of single stranded phage-host interactions, bridging classical genetics with modern viral discovery while establishing a robust host platform to access uncultured viral diversity and a functional blueprint to design next generation diagnostics, protein antibiotics, and biocontrol tools to halt horizontal gene transfer.

microbiology↗

bioRxiv2026

The ModelSEED Biochemistry Database, 2026 update: grading multi-source thermodynamics

The ModelSEED Biochemistry Database (https://modelseed.org) supplies foundational mass- and charge-balanced reaction networks for metabolic reconstructions. Here we present an update on the biochemistry where the database has been expanded to [~] 46,000 compounds, and [~] 56,000 reactions, featuring [~] 37,000 metabolic structures. We have expanded our approach for handling thermodynamic data, enabling multiple sources of data to be derived, integrated, and presented to the wider research community. We now publish predictions of pKa, reaction energy (and respective uncertainties), and estimates of reaction direction from multiple independent sources. Each reaction is graded gold, silver or bronze according to the strength of the evidence behind it, so that users can weigh its reliability directly. We also release reaction directions predicted by an ensemble of large language models. This multi-source approach exposes agreements and discrepancies between sources for [~] 33,000 reactions. Finally, to ensure data integrity, a new conflict-resolution pipeline reconciles structures across sources, documenting input from curators. Our work is publicly available at https://github.com/ModelSEED/ModelSEEDDatabase.

biochemistry↗

bioRxiv2026

A comprehensive phage-bacteria interaction atlas links phage lineage and capsule serotype to genome-guided machine learning prediction in Klebsiella pneumoniae

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

microbiology↗

bioRxiv2026

Data coherence over data volume drives generalisable genome-based prediction of microbial carbon utilisation

Microbial carbon utilisation is a foundational ecological phenotype that remains difficult to predict from genomes despite well-characterised pathways. Machine-learning models generalise poorly across datasets, a failure usually attributed to training-set size and taxonomic bias. To test this, we integrated binary growth phenotypes for 819 strains across 240 carbon sources from four datasets. Balanced accuracy fell from 0.86 within datasets to 0.62 across them, and testing on close relatives recovered only 0.03 of that drop, so mechanistically inconsistent genotype-phenotype relationships drove models to dataset-correlated shortcuts. Restricting training to concordant samples (measured growth matched their annotated pathway) doubled the carbon sources recovering known pathway genes across datasets (6 to 12 of 15), whereas matched random subsets did not. Adding over 8000 literature-curated BacDive genomes to the training set did not improve cross-dataset performance more than the smaller concordant set, suggesting coherence matters more than volume. Because such filtering requires a mechanistic predictor, we tested a mechanism-free alternative combining phylogenetic agreement and experimental labels, which recovered part of the gain but not the recall advantage. Concordance-trained models were bounded specialists, rescuing mechanistic false negatives twice as often as false positives (44\% versus 19\%), mostly metabolic generalists. To locate those bounds, model confidence defined an applicability domain, and prioritising low-confidence genomes for training improved cross-dataset accuracy more than random or diversity-based sampling, especially for the weaker phenotypes. This recasts generalisation in biological machine learning as a problem of label-mechanism agreement and applicability-domain definition, alongside data volume and algorithm choice.

bioinformatics↗

bioRxiv2026

Surface architecture of the bacterial envelope determines phage adsorption route in pathogenic Escherichia coli O157:H7

The outermost surface layers of Gram-negative bacteria determine phage access to terminal receptors, yet their genetic basis has been mapped almost exclusively in laboratory strains that lack them. Here we apply genome-wide RB-TnSeq fitness profiling to four Escherichia coli O157:H7 strains from distinct phylogenetic clades sharing the O157 O-antigen, using 38 phages with terminal receptors previously mapped in E. coli K-12 strain. RB-TnSeq fitness landscapes across all four pathogenic backgrounds were mostly similar, and dominated by surface-associated loci, including the gfc-etk group 4 capsule operon, O-antigen biosynthesis genes, LPS core assembly genes and outer membrane proteins. Disruption of gfc-etk abolished infection in 11 genetically diverse myoviruses, establishing the O-antigen capsule as a widespread required primary recognition substrate. O-antigen loci generated two classes of fitness score patterns. For 10 phages, disruption increased infectivity, indicating it is a barrier to receptor access; for 3 others, disruption abolished infectivity, demonstrating it can also be a primary recognition substrate. Outer membrane protein receptor identity was conserved across laboratory and pathogenic backgrounds, with the same proteins recognized in both K-12 and O157:H7, while glycan layer state determines whether these receptors are reached. These results demonstrate that outer surface glycan layers can act as primary and optional recognition substrates for phage infection, or as physical barriers preventing terminal receptor access. Extending the ability to probe phage-targeted receptors beyond outer membrane proteins provides a framework for incorporating glycan layer state into predictive models of phage-host interactions.

microbiology↗

bioRxiv2026

Enrichments along gradients resolve eco-evolutionary forces on subsurface microbiomes

How a single gram of soil harbors billions of microorganisms, each with distinct genomic variants that collectively maintain coherent ecological function(s), is one of microbiologys grand unsolved problems. A key obstacle is determining which variants contribute to individual- and community-level fitness, in which contexts, and how co-occurring ecotypes interact to divide niche space. Here, using nitrate (NO3-)-contaminated subsurface sediment as inoculum, we have performed high throughput enrichments in laboratory media of defined carbon source compositions across ecologically relevant gradients of pH and NO3-. Long-read metagenomics and link-community decomposition of co-occurrence networks of taxa across these enrichments has revealed context-specific functional interactions among dominant generalist and lower-abundance specialist denitrifier ecotypes that comprise 53 distinct enriched communities (EnComs) across 288 enrichments derived from a single sediment sample. We identified a single enzymatic difference of alternative NO3- reductases (NapAB vs. NarGHI) with differing substrate affinities that provided a mechanistic explanation for competitive niche partitioning between the two dominant taxa, Neorhizobium spp. and Allorhizobium spp., along the NO3- gradient. Genome-wide polymorphism ratios (pN/pS) revealed that selective pressures vary systematically with carbon source availability and gradients of pH and NO3-, which helps explain the natural biodiversity and functional interactions of ecotypes within denitrifying communities in the subsurface sediment. Our findings show that controlled enrichments along ecological gradients can thus uncover eco-evolutionary forces of selection, drift, and diversification that sculpt the biodiversity of microbial populations in the natural environment.

microbiology↗

bioRxiv2026

KBase Research Agent: Automated Multi-Agent Workflow Construction for Reproducible Genome Analysis

Constructing multi-step bioinformatics workflows, from read quality control through genome assembly to functional annotation, requires expertise in both biology and computational tool selection, creating a bottleneck for scalable and reproducible analysis. We present the KBase Research Agent, a multi-agent system for automating such workflows within the DOE Systems Biology Knowledgebase (KBase). Given a set of sequencing reads and a research objective, the agent constructs an analysis plan grounded in KBase documentation and a Knowledge Graph (KG) of the KBase application catalog, then selects, parameterizes, validates and executes appropriate KBase applications to carry out the workflow. The resulting analysis is preserved as a reproducible KBase Narrative. We evaluate the systems planning and execution quality against ground truth constructed from reference workflows derived from peer-reviewed Microbiology Resource Announcements. We further apply the agent to 100 previously unanalyzed bacterial isolate genomes from the JGI IMG/M database, where it autonomously performed read quality control, genome assembly, taxonomic classification with GTDB-Tk, and downstream analysis producing annotated genomes, reproducible Narratives, and draft manuscripts without human intervention. Across these experiments, the KBase Research Agent demonstrates the feasibility of domain-grounded, end-to-end scientific workflow automation in a production bioinformatics platform.

bioinformatics↗

bioRxiv2026

Comprehensive interaction profiling and machine learning prediction of bacteriophage infectivity across clinically diverse Pseudomonas aeruginosa

The rise of antibiotic-resistant bacterial infections has driven renewed interest in bacteriophage therapy, where viruses that specifically kill bacteria are used as targeted antimicrobials. Pseudomonas aeruginosa, a WHO critical-priority pathogen that causes severe infections in hospitalized and immunocompromised patients, presents a major challenge for phage therapy because of its extraordinary genetic diversity. Phages effective against one bacterial strain often fail against others, and existing cross-resistance-profiling approaches require iterative empirical testing of each new patient isolate. To establish a genome-based framework for rapid phage-isolate matching, we assembled a collection of 95 genomically diverse P. aeruginosa phages representing 20 genera and tested each against 99 genetically diverse clinical isolates, generating 9,405 infection outcome measurements. Bacterial O-antigen serotype emerged as the dominant determinant of strain susceptibility, while defense systems, anti-defense systems, and prophage burden contributed smaller strain-specific effects. The full curated multivariate model explained 47% of strain-susceptibility variance. Machine-learning models integrating these features and pangenome-derived gene clusters reached a per-strain AUROC of 0.86. In an in vivo proof-of-concept test against a single held-out strain, the ML-designed cocktail produced a [~]12-fold greater median CFU reduction than the expert-designed cocktail (q = 0.045), with both cocktails substantially reducing burden relative to the untreated control ([~]113-fold for ML, [~]9-fold for CG; both q < 10{square}3). SHAP analysis of the model identified bacterial surface-architecture genes (LPS biosynthesis, outer membrane proteins, type IV pili) as the dominant predictors, with defense-system content modulating which specific phages succeed against a strain rather than uniformly damping susceptibility. Together, these results establish a genome-based framework for predicting phage susceptibility in genetically diverse clinical isolates.

microbiology↗

bioRxiv2026

Integrative Modeling and Analysis of Fungal Central Carbon Metabolism

Over a thousand fungal genomes have been sequenced, yet manually curated genome-scale metabolic models (GEMs) are available for only a limited number of species. Moreover, these models have often been developed independently, leading to inconsistencies in namespaces, compartment definitions, and pathway representations that hinder comparative analysis, the systematic reuse of prior curation efforts, and the integration of consolidated metabolic knowledge. Here, we present the Consolidated Fungal Core Metabolism Model (CFCMM), constructed by integrating thirteen published fungal models spanning Ascomycota, Mucoromycota, and both Crabtree-positive and Crabtree-negative yeasts. We harmonized metabolites and reactions into a non-redundant shared ModelSEED ontological space, standardized compartmentalization, and refined gene-protein-reaction (GPR) rules. Using pathway-level visualization and systematic gap detection, we further improved the integrated network through literature-guided curation to correct stoichiometry, stereospecificity, and pathway architecture. Orthologous protein family reconstruction and functional annotation workflows were used to validate and inform GPR associations, with particular emphasis on ambiguous enzyme superfamilies and membrane-associated components. Using the resulting CFCMM, we built high-quality central carbon core models for each fungus and performed flux balance analysis to quantify ATP-yield variation under aerobic and anaerobic conditions, explicitly evaluating scenarios driven by differences in electron transport chain (ETC) composition. Simulations reproduced the expected fermentative yield of approximately 2 mmol ATP per mmol glucose under anaerobic conditions and separated the thirteen fungi into two bioenergetic groups under aerobic respiration based on Complex I status, with predicted yields of approximately 30 versus 22 mmol ATP per mmol glucose. Forcing flux through the alternative oxidase bypass further reduced ATP yields to approximately 12 and 4 mmol ATP per mmol glucose in Complex I-containing and Complex I-lacking fungi, respectively. Collectively, this work provides a manually curated, ModelSEED-consistent, and extensible fungal core metabolic template, deployed in DOE KBase as a resource for automated reconstruction of central carbon core models from any sequenced fungal genome. In addition, the CFCMM provides modular components for developing GEMs with more accurate energy predictions and enables robust comparative analyses of fungal bioenergetics and core metabolic diversity.

systems biology↗

bioRxiv2026

Single-cell carbon storage dynamics drive conditional fitness in microbes

Microbes frequently encounter fluctuating environments, requiring dynamic energy management strategies for survival. While carbon storage polymers like polyhydroxybutyrate (PHB) are ubiquitous across bacterial taxa, their precise ecological advantage remains poorly understood.1 Here we show that carbon storage drives conditional fitness during environmental transitions. Using a high-throughput single-cell microfluidic platform, we tracked tens of thousands of Cupriavidus necator cells under precisely controlled carbon and nitrogen fluctuations. We found that PHB provides no advantage under nutrient abundance but becomes decisive at starvation boundaries: during carbon starvation, it enables [~]30% more progeny before arrest; during recovery from nitrogen starvation, it shortens lag and accelerates regrowth. Strikingly, at the single-cell level, PHB granules are inherited in an asymmetric, all-or-nothing fashion, concentrating resources into specific lineages to overcome the discrete energetic threshold required for cell division. Despite this single-cell variance, at the population level, PHB fractions robustly return to a common setpoint after nutrient shifts--a homeostatic behavior consistent with integral feedback control. These findings reveal that while PHB does not increase the basal exponential growth rate, it confers a distinct fitness advantage by prolonging the proliferative phase during nutrient depletion and facilitating successful recovery from starvation, explaining the evolutionary persistence of carbon storage in environments with pulsed resource availability.

microbiology↗

bioRxiv2026

Enabling the prediction of phage receptor specificity from genome data

Predicting which receptor a phage binds to from genome sequence alone has remained an intractable challenge, principally because the experimental phenotypic data required to train and validate predictive models have not been available at sufficient scale. Here we address this by conducting 1,050 genome-wide genetic screens across 255 taxonomically diverse Escherichia coli dsDNA phages, assigning host receptors to 193 phages across 19 receptor classes. Comparative genomics and AlphaFold3 structural modelling resolved the sequence determinants of specificity to defined receptor-binding protein domains and individual residues. Machine learning models trained on this dataset predicted host receptor identity from phage genome sequence alone without prior annotation of receptor-binding genes, achieving perfect precision and greater than 80% recall on 49 independently validated phages, and yielding predictions for 1,060 of 1,875 E. coli phage genomes in NCBI. Domain swaps redirected receptor specificity as predicted, and a single amino acid substitution proved both necessary and sufficient to switch recognition between two distinct porins. These results demonstrate that systematic phenotyping at scale makes sequence-based prediction of molecular interaction specificity tractable, with direct implications for phage-based medicine, microbiome engineering and the broader challenge of inferring host-pathogen interaction outcomes from sequence.

microbiology↗

bioRxiv2026

The WalRK two-component system in Streptococcus pneumoniae ensures robustness of secondary wall polymer attachment

Capsular polysaccharide (CPS) is essential for Streptococcus pneumoniae virulence. Yet, the mechanism linking CPS to peptidoglycan (PG) remains unclear. Here, we identified a strong negative genetic interaction between the genes encoding the putative capsule ligase CpsA and the WalK histidine kinase, a component of the WalRK two-component system regulating cell wall homeostasis. In the absence of cpsA, capsule polymers compete with wall teichoic acids for ligase activity to PG. This induces cell wall stress and is sensed by the WalRK system. Overexpression of the PG hydrolase pcsB or disruption of the PG-modifying enzymes pgdA and oatA restored growth of strains lacking cpsA and walK. Furthermore, CpsA overproduction compensates for the loss of other LytR-Cps2A-Psr (LCP) ligases, suggesting it can support capsule and wall teichoic acid syntheses. These findings support the model that LCP ligases are semi-redundant, although they may install secondary polymers on a different residue of PG. This work also suggests that WalRK signaling compensates for reduced capsule and WTA attachment by positively regulating PG hydrolases. SIGNIFICANT STATEMENTStreptococcus pneumoniae causes approximately half a million deaths annually. A powerful public health tool for controlling pneumococcal infections is vaccination against the protective capsule. Yet, the mechanisms by which the capsule layer attaches to the underlying cell wall remain poorly defined. This study shows that the conserved capsule gene CpsA is not strictly required for capsule attachment but instead works together with other LytRIZCpsAIZPsr (LCP) ligases. However, it requires the essential WalRK signaling system to maintain cell envelope integrity. Defects in LCP activity are alleviated by WalRKIZdriven upregulation of peptidoglycan hydrolases, overexpression of PcsB, or inactivating peptidoglycan modifications that limit hydrolysis. These findings reveal coordination among flux to capsule synthesis, secondary wall polymer attachment, and cell wall remodeling. TEASERThe WalRK two-component system responds to cell envelope stress caused by reduced LCP ligase activity.

microbiology↗

bioRxiv2026

What microbes want: exploring microbial substrate preferences with the Web of Microbes Agent

Understanding and predicting bacterial substrate preferences has broad utility from microbial interactions to selecting prebiotics. Isolate exometabolite profiling directly measures which compounds a given microbe utilizes from an array of metabolites in the environment. However, modeling, mining, and integrating these data are challenging. Here, we introduce a Bayesian Personalized Ranking (BPR) model applied to substrate preferences which we find learns to rank compounds by a given microbes preference. It was found to outperform the other ranking models (AUC = 0.93), proved robust to ablation, showed strong within-genus isolate pairs correlation (Spearman rank = 0.78) and predictive ability for new data. BPR was then used to create the Web of Microbes (WoM) Agent by integrating it with the Phydon growth model and Large Language Model (LLM) for autonomous orchestration tool calling and analysis. The WoM Agent accurately predicted substrate consumption by existing strain grown on a novel medium and correctly identified bacteria enriched in soil metabolite spike-in experiments. Additionally, the WoM Agent can use autonomous reasoning including to predict substrates that will selectively promote the growth of one clade of bacteria over another including helping interpret results and suggest new hypotheses and experiments. We anticipate broad applications in microbial cultivation, microbiome engineering, and environmental microbiology, with the agents capabilities further extensible through the integration of additional tools and use of rapidly improving LLMs.

systems biology↗

bioRxiv2025

Construction of a randomly barcoded insertional mutant library in the filamentous fungus Trichoderma atroviride

Filamentous fungi play key roles in ecosystems, agriculture, biotechnology, symbiosis, and disease, yet the large-scale characterization of gene function in these organisms remains limited by low transformation efficiencies and their multinucleate, syncytial cells, which complicate high-throughput screening strategies. To address the challenge of high-throughput screening in filamentous fungi, we developed methods to construct a genome-wide barcoded insertional mutant library in Trichoderma atroviride, a filamentous fungus widely used as a biocontrol agent against bacterial and fungal plant pathogens. Our strategy leveraged randomly barcoded transfer DNA insertions from plasmid libraries containing hundreds of millions of unique DNA barcodes and a broad host-range drug resistance marker delivered via Agrobacterium tumefaciens into T. atroviride. By optimizing transformation conditions, we achieved up to 600 independent transformants per infection event, resulting in a library of over 31,000 mapped insertions disrupting 7,104 of the 11,863 predicted genes in the T. atroviride genome. This resource establishes a scalable platform for high-throughput functional genomics in filamentous fungi, enabling both fundamental investigations of fungal biology and engineering approaches toward improved medical applications, biotechnology, and sustainable agriculture.

systems biology↗

bioRxiv2025

Phylogeny-agnostic strain-level prediction of phage-host interactions from genomes

Bacteriophages offer promising alternatives to antibiotics for treating drug-resistant infections and engineering microbiomes, but applications are limited by inability to select phages infecting specific bacterial strains. Selecting suitable phages requires either one-to-one experimental assays or strain-level predictions of phage-host interactions. Existing computational approaches either predict host taxonomy at broad ranks unsuitable for strain-level targeting or require species-specific mechanistic knowledge limiting generalizability. Here, we present a phylogenyagnostic machine learning framework predicting strain-level phage-host interactions across diverse bacterial genera from genome sequences alone. Systematically optimizing the workflow over 13.2 million training runs across six datasets (115,037 interactions, 949 bacterial strains, 518 phages), we achieved performance matching species-specific methods (AUROC 0.67-0.94) while eliminating phylogenetic constraints. Comprehensive feature engineering identifies biologically interpretable genetic determinants while minimizing overfitting in sparse, imbalanced datasets. Experimental validation through 1,240 novel interactions confirmed generalizability (AUROC 0.84), while genome-wide RB-TnSeq screens verified that 68.6% of experimentally identified infection mediators were captured computationally, including receptors and cell wall biosynthesis pathways. Model-guided cocktail design achieved up to 97.5% bacterial coverage with five phages, and up to a 3.1-fold improvement in single-phage selection over promiscuity-based selection. This platform enables rational phage therapy design and precision microbiome engineering with applications in combating antimicrobial resistance across clinical, agricultural, and industrial contexts.

microbiology↗

bioRxiv2025

Droplet-based Random Barcode Transposon-site Sequencing (Droplet RB-TnSeq) to characterize phage-host interactions

Bacteriophages (phages), the most abundant self-replicating entities on Earth, are central to microbial ecology and hold promise as therapies against antibiotic-resistant pathogens. However, the molecular determinants of phage adaptation to hosts remain poorly defined. While bulk genetic screens such as transposon sequencing are high throughput, running all mutants in mixed populations biases outcomes toward phage receptor discovery. Competition among resistant mutants and the escalating multiplicity of infection (MOI) from additional phages released from lysed cells often mask moderately acting host factors--such as inner-membrane and energy-transduction systems supporting DNA entry, surface modifiers, and regulators of host defense. To overcome these limitations, we developed droplet-based random barcode transposon-site sequencing (Droplet RB-TnSeq), which encapsulates single barcoded mutant cells with defined phage titers. Applying this method to Escherichia coli infected with phages T4 and N4, we reproducibly recovered known determinants (ompC for T4 and nfrA/B for N4) and identified additional contributors, including membrane proteins, polysaccharide modifiers, signaling modules, and several uncharacterized genes. Notably, mutations in chbR/C/F, ygaZ/H, and yfbO/L were found to confer resistance to N4, validated by plaque assays and complementation. Droplet RB-TnSeq thus enables genome-wide dissection of host factors beyond receptor-level interactions under well-controlled infection conditions.

microbiology↗

bioRxiv2025

Novel origin of replication for environmentally isolated Pantoea strain enables expression of heterologous proteins, pathways and products

Plasmids isolated or characterized from environmental samples serve as a resource that can be used to develop genetic tools for characterizing recently isolated or less-studied microbes. In this report, we leveraged sequences from a previously characterized groundwater plasmidome and developed a screen to identify novel plasmid origins. Putative origin sequences were used to construct a barcoded plasmid library, which contained both known and newly predicted origins. This library was tested against a panel of representative bacterial strains and led to the identification of 3 novel origins that putatively replicate in gram-negative bacteria not previously associated with these origin sequences. We empirically validated one of the newly identified origins, 6911, to be functional in both the model bacterial strain, Escherichia coli BW25113, as well as in Pantoea sp. MT58, a fast growing and metal tolerant, environmentally important bacterium from the widespread Pantoea genus. We confirmed that a plasmid bearing origin 6911 as the sole origin could replicate and had a copy number of 9 ({+/-} 2) in Pantoea sp. MT58. We successfully used a plasmid based on the new origin to express the reporter protein GFP, and two non-native metabolite pathways for the natural product, indigoidine and the terpenoid compound, isoprenol. By pairing functional novel origins of replication to non-model organisms this pipeline can expand the tool kit for genetic manipulations of both model and less-studied bacteria. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=56 SRC="FIGDIR/small/653210v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@45689corg.highwire.dtl.DTLVardef@d6daeeorg.highwire.dtl.DTLVardef@1bf4d06org.highwire.dtl.DTLVardef@59ccfd_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOAbstract figure.C_FLOATNO We developed a host-origin pair identification pipeline by constructing a plasmid library containing both literature-sourced and computationally predicted origins of replication. By conjugating this library into non-model microbes, we identified functional origins in diverse bacteria, and could leverage these findings to develop genetically tractable hosts for microbial engineering. C_FIG

microbiology↗

bioRxiv2024

Modest functional diversity decline and pronounced composition shifts of microbial communities in a uranium-contaminated aquifer

BackgroundMicrobial taxonomic diversity declines with increased environmental stress. Yet, few studies have explored whether phylogenetic and functional diversities track taxonomic diversity along the stress gradient. Here, we investigated bacterial communities within an aquifer in Oak Ridge, Tennessee, USA, which is characterized by a broad spectrum of stressors, including extremely high levels of nitrate, heavy metals like cadmium and chromium, radionuclides such as uranium, and extremely low pH (<3). ResultsBoth taxonomic and phylogenetic -diversities were reduced in the most impacted wells, while the decline in functional -diversity was modest and statistically insignificant, indicating a more robust buffering capacity to environmental stress. Differences in functional gene composition (i.e., functional {beta}-diversity) were pronounced in highly contaminated wells, while convergent functional gene composition was observed in uncontaminated wells. The relative abundances of most carbon degradation genes were decreased in contaminated wells, but genes associated with denitrification, adenylylsulfate reduction, and sulfite reduction were increased. Compared to taxonomic and phylogenetic compositions, environmental variables played a more significant role in shaping functional gene composition, suggesting that niche selection could be more closely related to microbial functionality than taxonomy. ConclusionsOverall, we demonstrated that despite a reduced taxonomic -diversity, microbial communities under stress maintained functionality underpinned by environmental selection.

microbiology↗