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Published on: October 17, 2019
Burkholderia multivorans Exhibits Antibiotic Collateral Sensitivity
Jerilyn Nicole Flanagan1, Logan Kavanaugh1, Todd R Steck1
1Department of Biological Sciences, University of North Carolina at Charlotte, Charlotte, North Carolina.
This study reveals collateral sensitivity (CS) in Burkholderia multivorans, a bacterium causing chronic infections. Understanding CS and cross-resistance patterns can guide antibiotic cycling to combat multidrug resistance.
Area of Science:
- Microbiology
- Infectious Diseases
- Antibiotic Resistance
Background:
- * *Burkholderia multivorans* is part of the *Burkholderia cepacia* complex, known for antibiotic resistance and chronic lung infections in cystic fibrosis patients.
- * Collateral sensitivity (CS) is a phenomenon where resistance to one antibiotic confers sensitivity to another, offering a strategy against multidrug resistance.
- * Previous studies have identified CS in *Escherichia coli*, *Pseudomonas aeruginosa*, and *Staphylococcus aureus*.
Purpose of the Study:
- * To investigate collateral sensitivity (CS) and cross-resistance (CR) patterns in *Burkholderia multivorans*.
- * To map CS and CR networks for six key antibiotics.
- * To identify potential genes associated with CS in this bacterium.
Main Methods:
- * Bacterial evolution experiments were conducted using *Burkholderia multivorans*.
- * Resistance profiles were assessed against six antibiotics: ceftazidime, chloramphenicol, levofloxacin, meropenem, minocycline, and trimethoprim-sulfamethoxazole.
- * Genetic analysis was performed to identify candidate genes involved in CS.
Main Results:
- * *Burkholderia multivorans* demonstrated both collateral sensitivity (CS) and cross-resistance (CR).
- * Comprehensive CS and CR networks were established for the tested antibiotics.
- * Candidate genes implicated in CS mechanisms were identified.
Conclusions:
- * The study establishes CS and CR networks for *B. multivorans*, providing insights into antibiotic interactions.
- * Antibiotics can be clustered based on resistance evolution, suggesting a targeted therapy approach.
- * Understanding these patterns can inform strategies for managing chronic infections and combating antibiotic resistance.
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