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Author contributions: MG: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Visualization, Writing – original draft. IN: Investigation, Resources, Writing – review & editing. L-JB: investigation, Resources, Writing – review & editing. JG: Data curation, Funding acquisition, Resources, Supervision, Writing – review & editing. SO-P: Data curation, Funding acquisition, Resources, Writing – review & editing. AR: Data curation, Resources, Writing – review & editing. SO: Data curation, Funding acquisition, Resources, Software, Writing – review & editing. VM: Funding acquisition, Resources, Writing – review & editing. BM: Resources, Writing – review & editing. BG-Z: Funding acquisition, Writing – review & editing. MB: Funding acquisition, Resources, Writing – review & editing. LM: Funding acquisition, Resources, Writing – review & editing. BB: Resources, Writing – review & editing. KV: Resources, Software, Writing – review & editing. NR: Funding acquisition, Resources, Writing – review & editing. MA: Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing. SDK: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft., Metagenomic sequencing is a promising method that has the potential to revolutionize the world of pathogen detection and antimicrobial resistance (AMR) surveillance in food-producing environments. However, the analysis of the huge amount of data obtained requires performant bioinformatics tools and databases, with intuitive and straightforward interpretation. In this study, based on long-read metagenomics data of chicken fecal samples with a spike-in mock community, we proposed confidence levels for taxonomic identification and AMR gene detection, with interpretation guidelines, to help with the analysis of the output data generated by KMA, a popular k-mer read alignment tool. Additionally, we demonstrated that the completeness and diversity of the genomes present in the reference databases are key parameters for accurate and easy interpretation of the sequencing data. Finally, we explored whether KMA, in a two-step procedure, can be used to link the detected AMR genes to their bacterial host chromosome, both detected within the same long-reads. The confidence levels were successfully tested on 28 metagenomics datasets which were obtained with sequencing of real and spiked samples from fecal (chicken, pig, and buffalo) or food (minced beef and food enzyme products) origin. The methodology proposed in this study will facilitate the analysis of metagenomics sequencing datasets for KMA users. Ultimately, this will contribute to improvements in the rapid diagnosis and surveillance of pathogens and AMR genes in food-producing environments, as prioritized by the EU., European Comission, Depto. de Sanidad Animal, Fac. de Veterinaria, TRUE, pub

The current EU legislation on GMOs and GM food and feed requires analytical testing to support traceability of these products on the market. The European Network of GMO Laboratories has reviewed the implications of the analytical requirements when they are applied to plant products developed with the use of new genomic techniques, i.e. targeted mutagenesis and cisgenesis. This review concluded that analytical testing to support traceability is not considered feasible for all products obtained by targeted mutagenesis and cisgenesis, both due to technical restrictions and because of implementation issues.

Similar to genetically modified organisms (GMOs) produced by classical genetic engineering, gene-edited (GE) organisms and their derived food/feed products commercialized on the European Union market fall within the scope of European Union Directive 2001/18/EC. Consequently, their control in the food/feed chain by GMO enforcement laboratories is required by the competent authorities to guarantee food/feed safety and traceability (2003/1829/EC; 2003/1830/EC). However, their detection is potentially challenging at both the analytical and interpretation levels since this requires methodological approaches that can target and detect a specific single nucleotide variation (SNV) introduced into a GE organism. In this study, we propose a targeted high-throughput sequencing approach, including (i) a prior PCR-based enrichment step to amplify regions of interest, (ii) a sequencing step, and (iii) a data analysis methodology to identify SNVs of interest. To investigate if the performance of this targeted high-throughput sequencing approach is compatible with the performance criteria used in the GMO detection field, several samples containing different percentages of a GE rice line carrying a single adenosine insertion in OsMADS26 were prepared and analyzed. The SNV of interest in samples containing the GE rice line could successfully be detected, both at high and low percentages. No impact related to food processing or to the presence of other crop species was observed. The present proof-of-concept study has allowed us to deliver the first experimental-based evidence indicating that the proposed targeted high-throughput sequencing approach may constitute, in the future, a specific and sensitive tool to support the safety and traceability of the food/feed chain regarding GE plants carrying SNVs.