Antibiotic resistance among bacterial pathogens remains one of the great challenges confronting public health in the world today. Despite the remarkable success of antibiotics, bacterial infections remain one of the leading causes for mortality. Increasingly, sustained and broad use of antibiotics has selected for multi-drug resistant bacteria that adapt rapidly to newer generation antibiotics and shorten their clinical efficacy. We have developed a scalable and holistic approach that we call 'Quantitative Evolutionary Dynamics'(QED) to study daptomycin and tigecycline resistance in clinical isolates of vancomycin-resistant enterococci (VRE) and to tigecycline resistance in Acinetobacter baumannii. QED can be applied across many organisms and antibiotics to provide: 1) conceptual and mechanistic insights, 2) new targets for drug design, and 3) reveal the underlying biophysical basis for changes in cellular fitness leading to greater resistance during selection. To conduct QED, we use a combination of in vitro experimental evolution in turbidostats (fermentors that maintain bacterial populations at their fastest growth rate), genomic sequencing, DNA bar-coding to measure allelic frequencies (FREQ-SEQ), RNA-SEQ and physicochemical characterization, including X-ray crystallography, to provide an integrative approach to the identification and characterization of drug resistance targets and mechanisms. QED uses experimental evolution to identify the intermediates of adaptation to reconstruct the adaptive networks responsible for resistance. We use principles from evolutionary biology to rank the likely importance of such changes within the population and prioritize the most important targets for the more time consuming physical studies. QED shows excellent correspondence to in vivo clinical observations of antibiotic resistance. We produce insights not just into the clinically relevant strategies for resistance, but also the specfic biochemical mechanisms of resistance, the specific candidate genes responsible for those biochemical changes, and the basis for developing a quantitative link between those changes and the fitness (e.g. resistance) of the pathogen towards a specific drug. QED is a powerful and novel approach that can complement in vivo and clinical studies as well as reveal the evolutionary dynamics of antibiotic resistance.

Public Health Relevance

In this proposal we will identify the molecular mechanisms of daptomycin and tigecycline resistance in vancomycin-resistant enterococci (VRE) and to tigecycline resistance in Acinetobacter baumannii. We use a combination of experimental evolution and biophysics to explore how changes in the genome give rise to resistance and how these changes are brought about at the molecular level. We use that information to understand how adaptation to antibiotics happens and how we might develop drugs to limit adaptation and thereby increase the effectiveness of current and future antibiotics.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
High Priority, Short Term Project Award (R56)
Project #
2R56AI080714-05
Application #
8697252
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Huntley, Clayton C
Project Start
2013-08-01
Project End
2014-07-31
Budget Start
2013-08-01
Budget End
2014-07-31
Support Year
5
Fiscal Year
2013
Total Cost
$359,901
Indirect Cost
$124,901
Name
Rice University
Department
Biochemistry
Type
Schools of Arts and Sciences
DUNS #
050299031
City
Houston
State
TX
Country
United States
Zip Code
77005