Biology subjects

Dooley, D.

Publications and source records attributed to Dooley, D..

12 recordsLinked to original sources

bioRxiv2026

A Minimal Packaging Signal Enables Production of High-Purity Phage-Like Particles for CRISPR-Cas Antimicrobials in Staphylococcus aureus

Precision phage therapeutics provide a promising strategy to combat multidrug-resistant pathogens, including Staphylococcus aureus. Efficient, specific packaging of genetic cargoes remains challenging. Using modular design principles, we report a minimal phagemid packaging signal consisting of the phage terminase small subunit under its native promoter that significantly outperforms conventional packaging signals. The utility of this synthetic terS operon was demonstrated through production of highly concentrated and genetically pure CRISPR-Cas antimicrobials. To circumvent CRISPR-mediated self-targeting during antimicrobial generation, a terS-deficient strain was engineered to express the anti-CRISPR protein AcrIIA4, enabling titers above 1010 transducing units per milliliter (TRU/mL) with over 94% purity. With a high-copy origin of replication module, CRISPR-Cas phage-like particle titers could approach 1012 TRU/mL. We discovered that pure CRISPR-Cas antimicrobials are potent and can be amplified in hosts possessing prophages. Taken altogether, this study defines the minimal and optimal genetic requirements for efficient, specific creation of phage-based technologies. Technological ReadinessThe described system for engineering phage-like particles has reached a technological readiness level (TRL) of 4-5 based on the provided laboratory validation and strong literature support from other engineered phage therapies applied to in vivo models. Previous systems have demonstrated high-purity phage-like particle preparation, but always at the cost of severely reduced productivity. Therefore, our demarcation of a specific, efficient, and minimal system for packaging nucleic acid cargoes into phage vectors is a critical step toward real-world use. Despite this, low levels of contaminating host/phage DNA remain a key barrier to phage-based therapies. Protein and strain engineering efforts can help mitigate terminase nonspecificity, but care must be taken to not compromise productivity. More generally, widespread adoption will require deeper understanding of host-pathogen-phage interactions, development of scalable GMP manufacturing processes, and harmonized regulatory guidance that recognizes the dynamic nature of phage-derived technologies.

Synthetic Biology↗

bioRxiv2025

Proteome Reallocation Reveals Dynamics and Mechanisms of Phage Infection and Defense in Staphylococcus aureus

The coexistence of bacteria and phages is marked by dynamic interactions that determine infection outcomes. However, the mechanisms by which the host allocates its resources to cope with phage infection remain largely unknown. In this study, using longitudinal proteomics, we elucidated these interactions for the temperate staphylococcal phage{phi} NM1 and strains of Staphylococcus aureus either harboring its cognate prophage or lacking it. We demonstrated that infection of non-lysogenic S. aureus with{phi} NM1 induces a dramatic shutdown of host translation, reducing proteome allocation by over 20%. Quantitative analysis of the economics of{phi} NM1 infection revealed that the diversion of these cellular resources toward phage replication imposes a significant metabolic burden, thereby impairing cell growth. In contrast, lysogenic cells cope with phage infection and prevent culture collapse through a coordinated response of prophage-encoded defenses, host-encoded stress effectors, and reprogrammed cellular metabolism, thereby avoiding translation shutdown. Through coinfection with the wildtype phage and an engineered phage-like particle carrying a CRISPR-Cas phagemid, we revealed that synthetic DNA cargos evade host defenses and hijack the transcriptional machinery, altering infection outcomes. Without immunity, coinfection could collapse the non-lysogens more quickly than the native phage by overexpressing the cargo proteins, suppressing carbohydrate metabolism, and accelerating structural phage protein production through increased phage genome replication. Together, these findings provide a systems-level understanding of phage infection in S. aureus, uncovering the mechanisms for host takeover and prophage-mediated defense, with implications for next-generation phage therapy. SignificanceMultidrug-resistant bacteria pose a major threat to human health. Phage therapy offers a precise therapeutic approach by leveraging the specificity of phage infection and cargo delivery. However, as with conventional antibiotics, phage resistance can develop. Understanding the dynamic interactions between bacterial hosts and phages is therefore essential for predicting infection outcomes and designing precision phage therapies to suppress resistance. Using longitudinal proteomics, we elucidated the dynamics by which bacterial hosts reallocate cellular resources to cope with phage infection at the systems level. Coordinated defenses and reprogrammed metabolism are critical for host survival, but phage-delivered cargos can effectively bypass these barriers. These insights into the intricate interplay between the phage and host are crucial for designing next-generation precision phage therapies with greater lethality and less resistance.

systems biology↗

bioRxiv2025

GAME: Genomic API for Model Evaluation

The rapid expansion of genomics datasets and the application of machine learning has produced sequence-to-activity genomics models with ever-expanding capabilities. However, benchmarking these models on practical applications has been challenging because individual projects evaluate their models in ad hoc ways, and there is substantial heterogeneity of both model architectures and benchmarking tasks. To address this challenge, we have created GAME, a system for large-scale, community-led standardized model benchmarking on user-defined evaluation tasks. We borrow concepts from the Application Programming Interface (API) paradigm to allow for seamless communication between pre-trained models and benchmarking tasks, ensuring consistent evaluation protocols. Because all models and benchmarks are inherently compatible in this framework, the continual addition of new models and new benchmarks is easy. We also developed a Matcher module powered by a large language model (LLM) to automate ambiguous task alignment between benchmarks and models. Containerization of these modules enhances reproducibility and facilitates the deployment of models and benchmarks across computing platforms. By focusing on predicting underlying biochemical phenomena (e.g. gene expression, open chromatin, DNA binding), we ensure that tasks remain technology-independent. We provide examples of benchmarks and models implementing this framework, and anticipate that the community will contribute their own, leading to an ever-expanding and evolving set of models and evaluation tasks. This resource will accelerate genomics research by illuminating the best models for a given task, motivating novel functional genomic benchmarks, and providing a more nuanced understanding of model abilities.

bioinformatics↗

bioRxiv2025

VIRUS-MVP: A framework for comprehensive surveillance of viral mutations and their functional impacts

As viruses evolve, they accumulate genetic mutations that can influence disease severity, transmissibility, and the effectiveness of vaccines and therapeutics. Real-time tracking of viral mutations and their functional impacts is essential to understand these changes and assess their implications for public health responses. VIRUS-MVP is an interactive, portable platform designed for the comprehensive surveillance of viral mutations. Initially developed for SARS-CoV-2, it now fully supports mpox and is expanding to include influenza and RSV. The platform links viral mutations to functional annotations, providing insights into their predicted effects on viral infectivity, immune evasion, and protein functionality. It features an interactive interface for visualizing mutation distributions, a modular and reproducible genomics workflow, and a curated annotation resource that captures known impacts on viral proteins and host interactions. Users can also import custom functional annotations to tailor analyses to specific research needs or emerging pathogens. Developed collaboratively with public health and academic partners, VIRUS-MVP enhances understanding of viral evolution and its public health impact by bridging genomic data with biological insights. The platform is open-source, adaptable, and accessible on GitHub.

bioinformatics↗

bioRxiv2024

Expanded Genome and Proteome Reallocation in a Novel, Robust Bacillus coagulans Capable of Utilizing Pentose and Hexose Sugars

Bacillus coagulans, a Gram-positive thermophilic bacterium, is recognized for its probiotic properties and recent development as a microbial cell factory. Despite its importance for biotechnological applications, the current understanding of B. coagulans robustness is limited, especially for undomesticated strains. To fill this knowledge gap, we characterized the metabolic capability and performed functional genomics and systems analysis of a novel, robust strain, B. coagulans B-768. Genome sequencing revealed that B-768 has the largest B. coagulans genome known to date (3.94 Mbp), about 0.63 Mbp larger than the average genome of sequenced B. coagulans strains, with expanded carbohydrate metabolism and mobilome. Functional genomics identified a well-equipped genetic portfolio for utilizing a wide range of C5 (xylose, arabinose), C6 (glucose, mannose, galactose), and C12 (cellobiose) sugars present in biomass hydrolysates, which was validated experimentally. For growth on individual xylose and glucose, the dominant sugars in biomass hydrolysates, B-768 exhibited distinct phenotypes and proteome profiles. Faster growth and glucose uptake rates resulted in lactate overflow metabolism, which makes B. coagulans a lactate overproducer; however, slower growth and xylose uptake diminished overflow metabolism due to the high energy demand for sugar assimilation. Carbohydrate Transport and Metabolism (COG-G), Translation (COG-J), and Energy Conversion and Production (COG-C) made up 60-65% of the measured proteomes but were allocated differently when growing on xylose and glucose. The trade-off in proteome reallocation, with high investment in COG-C over COG-G, explains the xylose growth phenotype with significant upregulation of xylose metabolism, pyruvate metabolism, and TCA cycle. Strain B-768 tolerates and effectively utilizes inhibitory biomass hydrolysates containing mixed sugars and exhibits hierarchical sugar utilization with glucose as the preferential substrate. IMPORTANCEThe robustness of B. coagulans makes it a valuable microorganism for biotechnology applications, yet this phenotype is not well understood at the cellular level. Through phenotypic characterization and systems analysis, this study elucidates the functional genomics and robustness of a novel, undomesticated strain, B. coagulans B-768, capable of utilizing inhibitory switchgrass biomass hydrolysates. The genome of B-768, enriched with carbohydrate metabolism genes, demonstrates high regulatory capacity. The coordination of proteome reallocation in Carbohydrate Transport and Metabolism (COG-G), Translation (COG-J), and Energy Conversion and Production (COG-C) is critical for effective cell growth, sugar utilization, and lactate production via overflow metabolism. Overall, B-768 is a novel, robust, and promising B. coagulans strain that can be harnessed as a microbial biomanufacturing platform to produce chemicals and fuels from biomass hydrolysates.

systems biology↗

bioRxiv2023

NGBO: Introducing -omics metadata to biobanking ontology.

BackgroundWith improvements in high throughput sequencing technologies and the constant generation of large biomedical datasets, biobanks increasingly take on the role of managing and delivering not just specimens but also data. However, re-using data from different biobanks is challenged by incompatible data representations. Contextual data describing biobank digital resources often contain unstructured textual information incompatible with computational processes such as automated data discovery and integration. Therefore, a consistent and comprehensive contextual data framework is needed to increase discovery, reusability, and integrability across data sources. MethodsBased on available genomics standards (e.g., Minimum information about a microarray experiment (MIAME)), the College of American Pathologists (CAP) laboratory accreditation requirements, and the Open Biological and Biomedical Ontologies Foundry principles, we developed the Next Generation Biobanking Ontology (NGBO). In addition, we created new terms and re-used concepts from the Ontology for Biomedical Investigations (OBI) and the Ontology for Biobanking (OBIB) to build NGBO. ResultsThe Next Generation Biobanking Ontology https://www.ebi.ac.uk/ols4/ontologies/ngbo is an open application ontology representing omics contextual data, licensed under the Apache License 2.0. The ontology focuses on capturing information about three main activities: wet bench analysis used to generate omics data, bioinformatics analysis used to process and interpret data, and data management. In this paper, we demonstrated the use of the NGBO to add semantic statements to real-life use cases and query data previously stored in unstructured textual format.

bioinformatics↗

bioRxiv2022

A schema for digitized surface swab site metadata in open-source DNA sequence databases

Large, open-source DNA sequence databases have been generated, in part, through the collection of microbial pathogens from swabbing surfaces in built environments. Analyzing these data in aggregate through public health surveillance requires digitization of the complex, domain-specific metadata associated with swab site locations. However, the swab site location information is currently collected in a single, free-text "isolation source" field promoting generation of poorly detailed descriptions with varying word order, granularity, and linguistic errors, making automation difficult and reducing machine-actionability. We assessed 1,498 free-text swab site descriptions generated during routine foodborne pathogen surveillance. The lexicon of free-text metadata was evaluated to determine the informational facets and quantity of unique terms used by data collectors. Open Biological Ontologies (OBO) foundry libraries were used to develop hierarchical vocabularies connected with logical relationships to describe swab site locations. Five informational facets described by 338 unique terms were identified via content analysis. Term hierarchy facets were developed as were statements (called axioms) about how entities within these five domains were related. The schema developed through this study has been integrated into a publicly available pathogen metadata standard, facilitating ongoing surveillance and investigations. The One Health Enteric Package is available at NCBI BioSample beginning in 2022. Collective use of metadata standards increases the interoperability of DNA sequence databases, enabling large-scale approaches to data sharing, artificial intelligence, and big-data solutions to food safety. IMPORTANCERegular analysis of whole genome sequence data in collections such as NCBIs Pathogen Detection Database is used by many public health organizations to detect outbreaks of infectious disease. However, isolate metadata in these databases are often incomplete and poor quality. These complex raw metadata must often be re-organized and manually formatted for use in aggregate analysis. These processes are inefficient and time-consuming, increasing the interpretative labor needed by public health groups to extract actionable information. Future use of open genomic epidemiology networks will be supported through the development of an internationally applicable vocabulary system to describe swab site locations.

genomics↗

bioRxiv2022

Therapeutic administration of mouse mast cell protease 6 improves functional recovery after traumatic spinal cord injury in mice by promoting remyelination and reducing glial scar formation

Traumatic spinal cord injury (SCI) most often leads to permanent paralysis due to the inability of axons to regenerate in the adult mammalian central nervous system (CNS). In the past, we have shown that mast cells (MCs) improve the functional outcome after SCI by suppressing scar tissue formation at the lesion site via mouse mast cell protease 6 (mMCP6). In this study, we investigated whether recombinant mMCP6 can be used therapeutically to improve the functional outcome after SCI. Therefore, we applied mMCP6 locally via an intrathecal catheter in the subacute phase after a spinal cord hemisection injury in mice. Our findings showed that hind limb motor function was significantly improved in mice that received recombinant mMCP6 compared to the vehicle-treated group. In contrast to our previous findings in mMCP6 knockout mice, the lesion size and expression levels of the scar components fibronectin, laminin, and axon-growth-inhibitory chondroitin sulfate proteoglycans were not affected by the treatment with recombinant mMCP6. Surprisingly, no difference in infiltration of CD4+ T cells and reactivity of Iba-1+ microglia/macrophages at the lesion site was observed between the mMCP6-treated mice and control mice. Additionally, local protein levels of the pro- and anti-inflammatory mediators IL-1{beta}, IL-2, IL-4, IL-6, IL-10, TNF-, IFN{gamma}, and MCP-1 were comparable between the two treatment groups, indicating that locally applied mMCP6 did not affect inflammatory processes after injury. However, the increase in locomotor performance in mMCP6-treated mice was accompanied by reduced demyelination and astrogliosis in the perilesional area after SCI. Consistently, we found that TNF-a/IL-1B-astrocyte activation was decreased, and that oligodendrocyte precursor cell (OPC) differentiation was increased after recombinant mMCP6 treatment in vitro. Mechanistically, this suggests effects of mMCP6 on reducing astrogliosis and improving (re)myelination in the spinal cord after injury. In conclusion, these data show for the first time that recombinant mMCP6 is therapeutically active in enhancing recovery after SCI.

neuroscience↗

bioRxiv2022

Gene Co-expression Connectivity Predicts Gene Targets Underlying High Ionic Liquid Tolerance in Yarrowia lipolytica

Microbial tolerance to organic solvents such as ionic liquids (ILs) is a robust phenotype beneficial for novel biotransformation. While most microbes become inhibited in 1%-5% (v/v) IL (e.g., 1-ethyl-3-methylimidazolium acetate), we engineered a robust Yarrowia lipolytica (YlCW001) that tolerates a record high of 18% (v/v) IL via adaptive laboratory evolution. Yet, genotypes conferring high IL tolerance in YlCW001 remain to be discovered. Using dynamic RNA-Seq data, we shed light on the underlying cellular processes that Y. lipolytica responds to IL. We introduced Gene Co-expression Connectivity (GeCCo) to discover genotypes conferring desirable phenotypes that might not be found by the conventional differential expression (DE) approaches. GeCCo selects genes based on their number of co-expressed genes in a sub-network of upregulated genes by the target phenotype. We experimentally validated GeCCo by reversely engineering high IL-tolerant phenotype in wildtype Y. lipolytica. We found that gene targets selected by both DE and GeCCo exhibited the best statistical chance at increasing IL tolerance when individually overexpressed. Remarkably, the best combination of dual-overexpressed genes were genes selected by GeCCo alone. This non-intuitive combination of genes, BRN1 and OYE2, are involved in guiding/regulating mitotic cell division, chromatin segregation/condensation, microtubule and cytoskeletal organization, and Golgi vesicle transport.

systems biology↗

bioRxiv2021

OBO Foundry in 2021: Operationalizing Open Data Principles to Evaluate Ontologies

Biological ontologies are used to organize, curate, and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies (OBO) Foundry was created to address this by facilitating the development, harmonization, application, and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontologys compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data FAIR.

bioinformatics↗

bioRxiv2018

The Integrated Rapid Infectious Disease Analysis (IRIDA) Platform

Whole genome sequencing (WGS) is a powerful tool for public health infectious disease investigations owing to its higher resolution, greater efficiency, and cost-effectiveness over traditional genotyping methods. Implementation of WGS in routine public health microbiology laboratories is impeded by a lack of user-friendly automated and semi-automated pipelines, restrictive jurisdictional data sharing policies, and the proliferation of non-interoperable analytical and reporting systems. To address these issues, we developed the Integrated Rapid Infectious Disease Analysis (IRIDA) platform (irida.ca), a user-friendly, decentralized, open-source bioinformatics and analytical web platform to support real-time infectious disease outbreak investigations using WGS data. Instances can be independently installed on local high-performance computing infrastructure, enabling private and secure data management and analyses according to organizational policies and governance. IRIDAs data management capabilities enable secure upload, storage and sharing of all WGS data and metadata. The core platform currently includes pipelines for quality control, assembly, annotation, variant detection, phylogenetic analysis, in silico serotyping, multi-locus sequence typing, and genome distance calculation. Analysis pipeline results can be visualized within the platform through dynamic line lists and integrated phylogenomic clustering for research and discovery, and for enhancing decision-making support and hypothesis generation in epidemiological investigations. Communication and data exchange between instances are provided through customizable access controls. IRIDA complements centralized systems, empowering local analytics and visualizations for genomics-based microbial pathogen investigations. IRIDA is currently transforming the Canadian public health ecosystem and is freely available at https://github.com/phac-nml/irida and www.irida.ca.\n\nImpact StatementWhole genome sequencing (WGS) is revolutionizing infectious disease analysis and surveillance due to its cost effectiveness, utility, and improved analytical power. To date, no \"one-size-fits-all\" genomics platform has been universally adopted, owing to differences in national (and regional) health information systems, data sharing policies, computational infrastructures, lack of interoperability and prohibitive costs. The Integrated Rapid Infectious Disease Analysis (IRIDA) platform is a user-friendly, decentralized, open-source bioinformatics and analytical web platform developed to support real-time infectious disease outbreak investigations using WGS data. IRIDA empowers public health, regulatory and clinical microbiology laboratory personnel to better incorporate WGS technology into routine operations by shielding them from the computational and analytical complexities of big data genomics. IRIDA is now routinely used as part of a validated suite of tools to support outbreak investigations in Canada. While IRIDA was designed to serve the needs of the Canadian public health system, it is generally applicable to any public health and multi-jurisdictional environment. IRIDA enables localized analyses but provides mechanisms and standard outputs to enable data sharing. This approach can help overcome pervasive challenges in real-time global infectious disease surveillance, investigation and control, resulting in faster responses, and ultimately, better public health outcomes.\n\nDATA SUMMARYO_LIData used to generate some of the figures in this manuscript can be found in the NCBI BioProject PRJNA305824.\nC_LI

bioinformatics↗

bioRxiv2017

Antibiotic resistance genes in agriculture and urban influenced watersheds in southwestern British Columbia

BackgroundThe dissemination of antibiotic resistance genes (ARGs) from anthropogenic activities into the environment poses an emerging public health threat. Water constitutes a major vehicle for transport of both biological material and chemical substances. The present study focused on putative antibiotic resistance and integrase genes present in the microbiome of agricultural, urban influenced and protected watersheds in southwestern British Columbia, Canada. A metagenomics approach and high throughput quantitative PCR (HT qPCR) were used to screen for elements of resistance including ARGs and integron-associated integrase genes (intI). Sequencing of bacterial genomic DNA was used to characterize the resistome of microbial communities present in watersheds over a one-year period.\n\nResultsData mining using CARD and Integrall databases enabled the identification of putative antibiotic resistance genes present in watershed samples. Antibiotic resistance genes presence in samples from various watershed locations was low relative to the microbial population (<1 %). Analysis of the metagenomic sequences detected a total of 78 ARGs and intI1 across all watershed locations. The relative abundance and richness of antibiotic resistance genes was found to be highest in agriculture impacted watersheds compared to protected and urban watersheds. Gene copy numbers (GCNs) from a subset of 21 different elements of antibiotic resistance were further estimated using HT qPCR. Most GCNs of ARGs were found to be variable over time. A downstream transport pattern was observed in the impacted watersheds (urban and agricultural) during dry months. Urban and agriculture impacted sites had a higher GCNs of ARGs compared to protected sites. Similar to other reports, this study found a strong association between intI1 and ARGs (e.g., sul1), an association which may be used as a proxy for anthropogenic activities. Chemical analysis of water samples for three major groups of antibiotics was negative. However, the high richness and GCNs of ARGs in impacted sites suggest effects of effluents on microbial communities are occurring even at low concentrations of antimicrobials in the water column.\n\nConclusionAntibiotic resistance and integrase genes in a year-long metagenomic study showed that ARGs were driven mainly by environmental factors from anthropogenized sites in agriculture and urban watersheds. Environmental factors accounted for almost 40% of the variability observed in watershed locations.

microbiology↗