The construction of high capacity data sharing networks to support increasing government and commercial data exchange has highlighted a key roadblock: the content of existing Internet-connected information remains siloed due to a multiplicity of local languages and data dictionaries. This lack of a digital lingua franca is obvious in the domain of human food as materials travel from their wild or farm origin, through processing and distribution chains, to consumers. Well defined, hierarchical vocabulary, connected with logical relationships—in other words, an ontology—is urgently needed to help tackle data harmonization problems that span the domains of food security, safety, quality, production, distribution, and consumer health and convenience. FoodOn (http://foodon.org) is a consortium-driven project to build a comprehensive and easily accessible global farm-to-fork ontology about food, that accurately and consistently describes foods commonly known in cultures from around the world. FoodOn addresses food product terminology gaps and supports food traceability. Focusing on human and domesticated animal food description, FoodOn contains animal and plant food sources, food categories and products, and other facets like preservation processes, contact surfaces, and packaging. Much of FoodOn’s vocabulary comes from transforming LanguaL, a mature and popular food indexing thesaurus, into a World Wide Web Consortium (W3C) OWL Web Ontology Language-formatted vocabulary that provides system interoperability, quality control, and software-driven intelligence. FoodOn compliments other technologies facilitating food traceability, which is becoming critical in this age of increasing globalization of food networks.

With the growing importance of infectious diseases in health care and communicable disease outbreaks garnering increasing attention, new technologies are playing a greater role in helping us prevent health care-associated infections and provide optimal public health. The microbiology laboratory has always played a large role in infection control by providing tools to identify, characterize, and track pathogens. Recently, advances in DNA sequencing technology have ushered in a new era of genomic epidemiology, where traditional molecular diagnostics and genotyping methods are being enhanced and even replaced by genomics-based methods to aid epidemiologic investigations of communicable diseases. The ability to analyze and compare entire pathogen genomes has allowed for unprecedented resolution into how and why infectious diseases spread. As these genomics-based methods continue to improve in speed, cost, and accuracy, they will be increasingly used to inform and guide infection control and public health practices.

Background: The Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) is a global coalition that is actively working to establish consensus standards, document and share best practices, improve the availability of critical bioinformatics tools and resources, and advocate for greater openness, interoperability, accessibility, and reproducibility in public health microbial bioinformatics. In the face of the current pandemic, PHA4GE has identified a need for a fit-for-purpose, open-source SARS-CoV-2 contextual data standard. Results: As such, we have developed a SARS-CoV-2 contextual data specification package based on harmonizable, publicly available community standards. The specification can be implemented via a collection template, as well as an array of protocols and tools to support both the harmonization and submission of sequence data and contextual information to public biorepositories. Conclusions: Well-structured, rich contextual data add value, promote reuse, and enable aggregation and integration of disparate datasets. Adoption of the proposed standard and practices will better enable interoperability between datasets and systems, improve the consistency and utility of generated data, and ultimately facilitate novel insights and discoveries in SARS-CoV-2 and COVID-19. The package is now supported by the NCBI’s BioSample database.

Globalization of food networks increases opportunities for the spread of foodborne pathogens beyond borders and jurisdictions. High resolution whole-genome sequencing (WGS) subtyping of pathogens promises to vastly improve our ability to track and control foodborne disease, but to do so it must be combined with epidemiological, clinical, laboratory and other health care data (called “contextual data”) to be meaningfully interpreted for regulatory and health interventions, outbreak investigation, and risk assessment. However, current multi-jurisdictional pathogen surveillance and investigation efforts are complicated by time-consuming data re-entry, curation and integration of contextual information owing to a lack of interoperable standards and inconsistent reporting. A solution to these challenges is the use of 'ontologies' - hierarchies of well-defined and standardized vocabularies interconnected by logical relationships. Terms are specified by universal IDs enabling integration into highly regulated areas and multi-sector sharing (e.g. food and water microbiology with the veterinary sector). Institution-specific terms can be mapped to a given standard at different levels of granularity, maximizing comparability of contextual information according to jurisdictional policies. Fit-for-purpose ontologies provide contextual information with the auditability required for food safety laboratory accreditation. Our research efforts include the development of a Genomic Epidemiology Ontology (GenEpiO), and Food Ontology (FoodON) that harmonize important laboratory, clinical and epidemiological data fields, as well as existing food resources. These efforts are supported by a global consortium of researchers and stakeholders worldwide. Since foodborne diseases do not respect international borders, uptake of such vocabularies will be crucial for multi-jurisdictional interpretation of WGS results and data sharing.

Pathogen genomics is a critical tool for public health surveillance, infection control, outbreak investigations as well as research. In order to make use of pathogen genomics data, they must be interpreted using contextual data (metadata). Contextual data include sample metadata, laboratory methods, patient demographics, clinical outcomes and epidemiological information. However, the variability in how contextual information is captured by different authorities and how it is encoded in different databases poses challenges for data interpretation, integration and their use/re-use. The DataHarmonizer is a template-driven spreadsheet application for harmonizing, validating and transforming genomics contextual data into submission-ready formats for public or private repositories. The tool’s web browser-based JavaScript environment enables validation and its offline functionality and local installation increases data security. The DataHarmonizer was developed to address the data sharing needs that arose during the COVID-19 pandemic, and was used by members of the Canadian COVID Genomics Network (CanCOGeN) to harmonize SARS-CoV-2 contextual data for national surveillance and for public repository submission. In order to support coordination of international surveillance efforts, we have partnered with the Public Health Alliance for Genomic Epidemiology to also provide a template conforming to its SARS-CoV-2 contextual data specification for use worldwide. Templates are also being developed for One Health and foodborne pathogens. Overall, the DataHarmonizer tool improves the effectiveness and fidelity of contextual data capture as well as its subsequent usability. Harmonization of contextual information across authorities, platforms and systems globally improves interoperability and reusability of data for concerted public health and research initiatives to fight the current pandemic and future public health emergencies. While initially developed for the COVID-19 pandemic, its expansion to other data management applications and pathogens is already underway.

Introduction. Mycobacterium abscessus (MABS) is a pathogenic bacterium that can cause severe lung infections, particularly in individuals with cystic fibrosis. MABS colonies can exhibit either a smooth (S) or rough (R) morphotype, influenced by the presence or absence of glycopeptidolipids (GPLs) on their surface, respectively. Despite the clinical significance of these morphotypes, the relationship between GPL levels, morphotype and the pathogenesis of MABS infections remains poorly understood. Gap statement. The mechanisms and implications of GPL production and morphotypes in clinical MABS infections are unclear. There is a gap in understanding their correlation with infectivity and pathogenicity, particularly in patients with underlying lung disease. Aim. This study aimed to investigate the correlation between MABS morphology, GPL and infectivity by analysing strains from cystic fibrosis patients’ sputum samples. Methodology. MABS was isolated from patient sputum samples and categorized by morphotype, GPL profile and replication rate in macrophages. A high-content ex vivo infection model using THP-1 cells assessed the infectivity of both clinical and laboratory strains. Results. Our findings revealed that around 50 % of isolates displayed mixed morphologies. GPL analysis confirmed a consistent relationship between GPL content and morphotype that was only found in smooth isolates. Across morphotype groups, no differences were observed in vitro, yet clinical R strains were observed to replicate at higher levels in the THP-1 infection model. Moreover, the proportion of infected macrophages was notably higher among clinical R strains compared to their S counterparts at 72 h post-infection. Clinical variants also infected THP-1 cells at significantly higher rates compared to laboratory strains, highlighting the limited translatability of lab strain infection data to clinical contexts. Conclusion. Our study confirmed the general correlation between morphotype and GPL levels in smooth strains yet unveiled more variability within morphotype groups than previously recognized, particularly during intracellular infection. As the R morphotype is the highest clinical concern, these findings contribute to the expanding knowledge base surrounding MABS infections, offering insights that can steer diagnostic methodologies and treatment approaches.