High-throughput method characterizes hundreds of previously unknown antibiotic resistance mutations
Abstract A fundamental obstacle to tackling the antimicrobial resistance crisis is identifying mutations that lead to resistance in a given genomic background and environment. We present a high-throughput technique – Quantitative Mutational Scan sequencing (QMS-seq) – that enables quantitative compa...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56050-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832594551849615360 |
|---|---|
| author | Matthew J. Jago Jake K. Soley Stepan Denisov Calum J. Walsh Danna R. Gifford Benjamin P. Howden Mato Lagator |
| author_facet | Matthew J. Jago Jake K. Soley Stepan Denisov Calum J. Walsh Danna R. Gifford Benjamin P. Howden Mato Lagator |
| author_sort | Matthew J. Jago |
| collection | DOAJ |
| description | Abstract A fundamental obstacle to tackling the antimicrobial resistance crisis is identifying mutations that lead to resistance in a given genomic background and environment. We present a high-throughput technique – Quantitative Mutational Scan sequencing (QMS-seq) – that enables quantitative comparison of which genes are under antibiotic selection and captures how genetic background influences resistance evolution. We compare four E. coli strains exposed to ciprofloxacin, cycloserine, or nitrofurantoin and identify 812 resistance mutations, many in genes and regulatory regions not previously associated with resistance. We find that multi-drug and antibiotic-specific resistance are acquired through categorically different types of mutations, and that minor genotypic differences significantly influence evolutionary routes to resistance. By quantifying mutation frequency with single base pair resolution, QMS-seq informs about the underlying mechanisms of resistance and identifies mutational hotspots within genes. Our method provides a way to rapidly screen for resistance mutations while assessing the impact of multiple confounding factors. |
| format | Article |
| id | doaj-art-7023519bfc6148b88d364a76aa828c32 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-7023519bfc6148b88d364a76aa828c322025-01-19T12:32:12ZengNature PortfolioNature Communications2041-17232025-01-0116111310.1038/s41467-025-56050-2High-throughput method characterizes hundreds of previously unknown antibiotic resistance mutationsMatthew J. Jago0Jake K. Soley1Stepan Denisov2Calum J. Walsh3Danna R. Gifford4Benjamin P. Howden5Mato Lagator6Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of ManchesterDivision of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of ManchesterDivision of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of ManchesterDepartment of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and ImmunityDivision of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of ManchesterDepartment of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and ImmunityDivision of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of ManchesterAbstract A fundamental obstacle to tackling the antimicrobial resistance crisis is identifying mutations that lead to resistance in a given genomic background and environment. We present a high-throughput technique – Quantitative Mutational Scan sequencing (QMS-seq) – that enables quantitative comparison of which genes are under antibiotic selection and captures how genetic background influences resistance evolution. We compare four E. coli strains exposed to ciprofloxacin, cycloserine, or nitrofurantoin and identify 812 resistance mutations, many in genes and regulatory regions not previously associated with resistance. We find that multi-drug and antibiotic-specific resistance are acquired through categorically different types of mutations, and that minor genotypic differences significantly influence evolutionary routes to resistance. By quantifying mutation frequency with single base pair resolution, QMS-seq informs about the underlying mechanisms of resistance and identifies mutational hotspots within genes. Our method provides a way to rapidly screen for resistance mutations while assessing the impact of multiple confounding factors.https://doi.org/10.1038/s41467-025-56050-2 |
| spellingShingle | Matthew J. Jago Jake K. Soley Stepan Denisov Calum J. Walsh Danna R. Gifford Benjamin P. Howden Mato Lagator High-throughput method characterizes hundreds of previously unknown antibiotic resistance mutations Nature Communications |
| title | High-throughput method characterizes hundreds of previously unknown antibiotic resistance mutations |
| title_full | High-throughput method characterizes hundreds of previously unknown antibiotic resistance mutations |
| title_fullStr | High-throughput method characterizes hundreds of previously unknown antibiotic resistance mutations |
| title_full_unstemmed | High-throughput method characterizes hundreds of previously unknown antibiotic resistance mutations |
| title_short | High-throughput method characterizes hundreds of previously unknown antibiotic resistance mutations |
| title_sort | high throughput method characterizes hundreds of previously unknown antibiotic resistance mutations |
| url | https://doi.org/10.1038/s41467-025-56050-2 |
| work_keys_str_mv | AT matthewjjago highthroughputmethodcharacterizeshundredsofpreviouslyunknownantibioticresistancemutations AT jakeksoley highthroughputmethodcharacterizeshundredsofpreviouslyunknownantibioticresistancemutations AT stepandenisov highthroughputmethodcharacterizeshundredsofpreviouslyunknownantibioticresistancemutations AT calumjwalsh highthroughputmethodcharacterizeshundredsofpreviouslyunknownantibioticresistancemutations AT dannargifford highthroughputmethodcharacterizeshundredsofpreviouslyunknownantibioticresistancemutations AT benjaminphowden highthroughputmethodcharacterizeshundredsofpreviouslyunknownantibioticresistancemutations AT matolagator highthroughputmethodcharacterizeshundredsofpreviouslyunknownantibioticresistancemutations |