Towards facilitated interpretation of shotgun metagenomics long-read sequencing data analyzed with KMA for the detection of bacterial pathogens and their antimicrobial resistance genes.

Autor:	Gand M; Transversal Activities in Applied Genomics, Sciensano, Brussels, Belgium., Navickaite I; Department of Bacteriology, Animal and Plant Health Agency, Weybridge, United Kingdom., Bartsch LJ; Department of Biological Safety, German Federal Institute for Risk Assessment, Berlin, Germany., Grützke J; Department of Biological Safety, German Federal Institute for Risk Assessment, Berlin, Germany., Overballe-Petersen S; Bacterial Reference Center, Statens Serum Institute, Copenhagen, Denmark., Rasmussen A; Bacterial Reference Center, Statens Serum Institute, Copenhagen, Denmark., Otani S; National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark., Michelacci V; Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy., Matamoros BR; Department of Animal Health, Complutense University of Madrid, Madrid, Spain., González-Zorn B; Department of Animal Health, Complutense University of Madrid, Madrid, Spain., Brouwer MSM; Wageningen Bioveterinary Research Part of Wageningen University and Research, Lelystad, Netherlands., Di Marcantonio L; Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise 'G. Caporale', Teramo, Italy., Bloemen B; Transversal Activities in Applied Genomics, Sciensano, Brussels, Belgium., Vanneste K; Transversal Activities in Applied Genomics, Sciensano, Brussels, Belgium., Roosens NHCJ; Transversal Activities in Applied Genomics, Sciensano, Brussels, Belgium., AbuOun M; Department of Bacteriology, Animal and Plant Health Agency, Weybridge, United Kingdom., De Keersmaecker SCJ; Transversal Activities in Applied Genomics, Sciensano, Brussels, Belgium.
Jazyk:	angličtina
Zdroj:	Frontiers in microbiology [Front Microbiol] 2024 Apr 04; Vol. 15, pp. 1336532. Date of Electronic Publication: 2024 Apr 04 (Print Publication: 2024).
DOI:	10.3389/fmicb.2024.1336532
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. (Copyright © 2024 Gand, Navickaite, Bartsch, Grützke, Overballe-Petersen, Rasmussen, Otani, Michelacci, Matamoros, González-Zorn, Brouwer, Di Marcantonio, Bloemen, Vanneste, Roosens, AbuOun and De Keersmaecker.)
Databáze:	MEDLINE
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