MultiSeq-AMR: a modular amplicon-sequencing workflow for rapid detection of bloodstream infection and antimicrobial resistance markers
Bloodstream infections (BSIs) represent a significant global health challenge, and traditional diagnostic methods are suboptimal for timely guiding targeted antibiotic therapy. We introduce MultiSeq-AMR, a rapid and modular nanopore amplicon-sequencing workflow to identify bacterial and fungal species and a comprehensive set of antimicrobial resistance (AMR) genes (<i>n</i>=91) from various types of infection sources. We initially benchmarked MultiSeq-AMR using DNA from 16 bacterial and 5 fungal reference strains and accurately identified all species. AMR gene identification exhibited 99.4% categorical agreement (CA: 153/154 prediction) with whole-genome sequencing. Further validation with 33 BACT/ALERT positive samples from suspected BSI cases revealed 100% accuracy for genus and 96.7% for species identification, with 97.4% CA (151/155) for AMR gene prediction. To accelerate microbiological diagnosis, a 6 h culture enrichment step was tested with MultiSeq-AMR using 15 clinically important bacterial species. Of 13 species selected for sequencing, 11 were correctly identified, with 96% CA (59/61 predictions) for AMR gene identification. With only 2 Mbp yield, sequencing identified 93.7% of species and 89.8% AMR genes initially detected with 20-50 Mbp yield/sample. MultiSeq-AMR holds promise for BSI diagnosis, as species/AMR genes could be identified under 5 h of BACT/ALERT positivity and potentially <11 h of sample collection (rapid-enrichment) for a large set of bacterial species. MultiSeq-AMR gene targets can be modified/increased indefinitely to suit user needs. Further research is required to clinically validate MultiSeq-AMR, especially the rapid enrichment method, to assess its utility in a medical setup and in improving patient outcomes in BSI.
Authors
- Kirstyn Brunker (ORCID: https://orcid.org/0000-0001-9990-6299)
- Ma Jowina H. Galarion (ORCID: https://orcid.org/0000-0003-0088-2093)
- Paul Everest
- Mohammad Saiful Islam Sajib (ORCID: https://orcid.org/0000-0002-3962-9805)
- Catherine Wilson (ORCID: https://orcid.org/0000-0001-6665-9391)
- Manuel Fuentes (ORCID: https://orcid.org/0000-0001-9253-316X)
- Taya Forde (ORCID: https://orcid.org/0000-0001-9058-7826)
- Katarína Oravcová (ORCID: https://orcid.org/0000-0001-5930-6803)
- Michael E. Murphy (ORCID: https://orcid.org/0000-0002-3161-6612)
Institutions
- NHS Greater Glasgow and Clyde (GB)
- Glasgow Royal Infirmary (GB)
- MRC University of Glasgow Centre for Virus Research (GB)
- University of Glasgow (GB)
Publication Details
- Journal
- Microbial Genomics
- Published
- 2025-04-03
- DOI
- https://doi.org/10.1099/mgen.0.001383
- Citations
- 1
- Primary Topic
- Bacterial Identification and Susceptibility Testing
- Type
- article
- Field-Weighted Citation Impact
- 1.32
Funders
- Royal Society
- School of Veterinary Medicine, University of Glasgow