Inference of antimicrobial resistance (AMR) from a whole genome database outperforming AMR gene detection
Summary: This study focuses on the rapid detection of antimicrobial resistance (AMR) in Klebsiella pneumoniae. The “Align-Search-Infer” pipeline aligned query sequences from 24 urine samples against a curated genome database of 40 Klebsiella isolates, searched for the best matches, and inferred thei...
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Elsevier
2025-08-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225012234 |
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| author | Pornsawan Cholsaktrakool Kornthara Kawang Nicha Sangpiromapichai Pannaporn Thongsuk Songtham Anuntakarun Pattapon Kunadirek Natthaya Chuaypen Sumanee Nilgate Naris Kueakulpattana Ubolrat Rirerm Tanittha Chatsuwan Elita Jauneikaite Frances Davies Ploy N. Pratanwanich Sira Sriswasdi Voraphoj Nilaratanakul |
| author_facet | Pornsawan Cholsaktrakool Kornthara Kawang Nicha Sangpiromapichai Pannaporn Thongsuk Songtham Anuntakarun Pattapon Kunadirek Natthaya Chuaypen Sumanee Nilgate Naris Kueakulpattana Ubolrat Rirerm Tanittha Chatsuwan Elita Jauneikaite Frances Davies Ploy N. Pratanwanich Sira Sriswasdi Voraphoj Nilaratanakul |
| author_sort | Pornsawan Cholsaktrakool |
| collection | DOAJ |
| description | Summary: This study focuses on the rapid detection of antimicrobial resistance (AMR) in Klebsiella pneumoniae. The “Align-Search-Infer” pipeline aligned query sequences from 24 urine samples against a curated genome database of 40 Klebsiella isolates, searched for the best matches, and inferred their antimicrobial susceptibility. Carbapenem resistance inference achieved 77.3% accuracy (95%CI: 59.8–94.8%) within 10 min using whole-genome matching, and 85.7% accuracy (95%CI: 70.7–100.0%) within 1 h using plasmid matching — both surpassing the 54.2% accuracy (95%CI: 34.2–74.1%) of AMR gene detection at 6 h. The proposed method requires less bacterial DNA and is suitable for low-load clinical samples. Our small local database performed comparably to large public databases. This study supports the integration of pathogen-specific genome databases into clinical workflows to enable rapid and accurate antimicrobial susceptibility prediction. Further research is needed to validate and refine the method using larger genomic-phenotypic datasets across diverse pathogens and sample types. |
| format | Article |
| id | doaj-art-563729b20c404fdeb6697dcaee795e17 |
| institution | DOAJ |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-563729b20c404fdeb6697dcaee795e172025-08-20T03:16:57ZengElsevieriScience2589-00422025-08-0128811296210.1016/j.isci.2025.112962Inference of antimicrobial resistance (AMR) from a whole genome database outperforming AMR gene detectionPornsawan Cholsaktrakool0Kornthara Kawang1Nicha Sangpiromapichai2Pannaporn Thongsuk3Songtham Anuntakarun4Pattapon Kunadirek5Natthaya Chuaypen6Sumanee Nilgate7Naris Kueakulpattana8Ubolrat Rirerm9Tanittha Chatsuwan10Elita Jauneikaite11Frances Davies12Ploy N. Pratanwanich13Sira Sriswasdi14Voraphoj Nilaratanakul15Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, ThailandDivision of Infectious Diseases, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand; Excellence Center for Infectious Diseases, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, ThailandMaster of Science Program in Medical Sciences, Faculty of Medicine, Chulalongkorn University, Bangkok, ThailandDivision of Infectious Diseases, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, ThailandCenter of Excellence in Hepatitis and Liver Cancer, Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, ThailandCenter of Excellence in Hepatitis and Liver Cancer, Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; Bumrungrad International Hospital, Bangkok, ThailandMetabolic Diseases in Gut and Urinary System Research Unit (MeDGURU), Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, ThailandDepartment of Microbiology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, ThailandDepartment of Microbiology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand; Center of Excellence in Antimicrobial Resistance and Stewardship, Faculty of Medicine, Chulalongkorn University, Bangkok, ThailandDepartment of Microbiology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, ThailandDepartment of Microbiology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand; Center of Excellence in Antimicrobial Resistance and Stewardship, Faculty of Medicine, Chulalongkorn University, Bangkok, ThailandNIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, UKNIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London, UK; Imperial College Healthcare NHS Trust, London, UK; Department of Microbiology, North West London Pathology, London, UKDepartment of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand; Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, ThailandCenter of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; Center for Artificial Intelligence in Medicine, Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; Corresponding authorDivision of Infectious Diseases, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand; Excellence Center for Infectious Diseases, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand; Healthcare-associated Infection Research Group STAR (Special Task Force for Activating Research), Chulalongkorn University, Bangkok, Thailand; Corresponding authorSummary: This study focuses on the rapid detection of antimicrobial resistance (AMR) in Klebsiella pneumoniae. The “Align-Search-Infer” pipeline aligned query sequences from 24 urine samples against a curated genome database of 40 Klebsiella isolates, searched for the best matches, and inferred their antimicrobial susceptibility. Carbapenem resistance inference achieved 77.3% accuracy (95%CI: 59.8–94.8%) within 10 min using whole-genome matching, and 85.7% accuracy (95%CI: 70.7–100.0%) within 1 h using plasmid matching — both surpassing the 54.2% accuracy (95%CI: 34.2–74.1%) of AMR gene detection at 6 h. The proposed method requires less bacterial DNA and is suitable for low-load clinical samples. Our small local database performed comparably to large public databases. This study supports the integration of pathogen-specific genome databases into clinical workflows to enable rapid and accurate antimicrobial susceptibility prediction. Further research is needed to validate and refine the method using larger genomic-phenotypic datasets across diverse pathogens and sample types.http://www.sciencedirect.com/science/article/pii/S2589004225012234Natural sciencesBiological sciencesMicrobiologyMicrobial genomics |
| spellingShingle | Pornsawan Cholsaktrakool Kornthara Kawang Nicha Sangpiromapichai Pannaporn Thongsuk Songtham Anuntakarun Pattapon Kunadirek Natthaya Chuaypen Sumanee Nilgate Naris Kueakulpattana Ubolrat Rirerm Tanittha Chatsuwan Elita Jauneikaite Frances Davies Ploy N. Pratanwanich Sira Sriswasdi Voraphoj Nilaratanakul Inference of antimicrobial resistance (AMR) from a whole genome database outperforming AMR gene detection iScience Natural sciences Biological sciences Microbiology Microbial genomics |
| title | Inference of antimicrobial resistance (AMR) from a whole genome database outperforming AMR gene detection |
| title_full | Inference of antimicrobial resistance (AMR) from a whole genome database outperforming AMR gene detection |
| title_fullStr | Inference of antimicrobial resistance (AMR) from a whole genome database outperforming AMR gene detection |
| title_full_unstemmed | Inference of antimicrobial resistance (AMR) from a whole genome database outperforming AMR gene detection |
| title_short | Inference of antimicrobial resistance (AMR) from a whole genome database outperforming AMR gene detection |
| title_sort | inference of antimicrobial resistance amr from a whole genome database outperforming amr gene detection |
| topic | Natural sciences Biological sciences Microbiology Microbial genomics |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225012234 |
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