Tobias Ambjörnsson
Senior lecturer
Competitive binding-based optical DNA mapping for fast identification of bacteria - multi-ligand transfer matrix theory and experimental applications on Escherichia coli.
Author
Summary, in English
We demonstrate a single DNA molecule optical mapping assay able to resolve a specific Escherichia coli strain from other strains. The assay is based on competitive binding of the fluorescent dye YOYO-1 and the AT-specific antibiotic netropsin. The optical map is visualized by stretching the DNA molecules in nanofluidic channels. We optimize the experimental conditions to obtain reproducible barcodes containing as much information as possible. We implement a multi-ligand transfer matrix method for calculating theoretical barcodes from known DNA sequences. Our method extends previous theoretical approaches for competitive binding of two types of ligands to many types of ligands and introduces a recursive approach that allows long barcodes to be calculated with standard computer floating point formats. The identification of a specific E. coli strain (CCUG 10979) is based on mapping of 50-160 kilobasepair experimental DNA fragments onto the theoretical genome using the developed theory. Our identification protocol introduces two theoretical constructs: a P-value for a best experiment-theory match and an information score threshold. The developed methods provide a novel optical mapping toolbox for identification of bacterial species and strains. The protocol does not require cultivation of bacteria or DNA amplification, which allows for ultra-fast identification of bacterial pathogens.
Department/s
- Department of Physics
- Computational Biology and Biological Physics - Has been reorganised
- Solid State Physics
Publishing year
2014
Language
English
Pages
118-118
Publication/Series
Nucleic Acids Research
Volume
42
Issue
15
Document type
Journal article
Publisher
Oxford University Press
Topic
- Biophysics
- Condensed Matter Physics
- Other Physics Topics
Status
Published
ISBN/ISSN/Other
- ISSN: 1362-4962