Abstract:Antibiotics are nanometer sized natural or synthetic small molecules that are used for curing bacterial infections. The discovery of penicillin by Alexander Fleming is one of the greatest achievements in human history that stopped the spread of bacterial diseases which once killed millions of humans and animals. However, several pathogenic bacteria eventually acquired resistance against antibiotics due to uncontrolled clinical and agricultural antibiotic use. As a result, fighting against infections caused by drug resistant bacteria with currently available antibiotics became very difficult. Prolonged treatment times because of drug resistance caused human suffering, loss of labor, more frequent hospital admissions, and higher health care costs. Producing novel antibiotics and/or using currently available antibiotics more effectively are two proposed solutions for this important problem. Unfortunately, as of today, none of these two potential solutions have contributed enough to dealing with the resistance problem mainly due to our limited knowledge of the genetic basis and the population dynamics of bacterial drug resistance. To contribute to the solution of this important health problem, we have developed an automated microbial selection device, the “morbidostat”, which dynamically adjusts drug concentrations to constantly inhibit bacterial populations as they become more and more resistant. By using the morbidostat, we have investigated the genetic trajectories that increase resistance to three clinically important drugs: chloramphenicol, doxycycline and trimethoprim. Over a period of 3 weeks, the resistance levels increased dramatically (1000, 10 and 1700 -fold respectively) with parallel propagating bacterial cultures showing very similar phenotypic trajectories in each drug. Whole-genome sequencing of the bacterial populations that acquired resistance identified both drug-specific and drug-general genetic changes. Mutations in membrane proteins and genes involved in translation and transcription appeared in the chloramphenicol and doxycycline resistant populations, whereas almost all mutations conferring trimethoprim-resistance appeared on the target enzyme, dihydrofolate reductase (DHFR). Sequencing the coding and non-coding regions of DHFR from multiple clones sampled daily along the experiment revealed that the specific residues mutated and their order of appearance is similar among parallel evolved populations. The capacity of microbes to evolve strong antibiotic resistance through sequential acquisition of mutations along confined evolutionary paths may suggest future avenues for blocking available paths to resistance.
Reference: Erdal Toprak, Adrian Veres, Jean-Baptiste Michel, Remy Chait, Daniel L. Hartl, Roy Kishony, “Evolutionary paths to strong antibiotic resistance under dynamically sustained drug stress”, Nature Genetics, 2012, DOI: 10.1038/ng.1034.
Short Bio: Erdal Toprak received both his BS and MS degrees in physics from Bogazici University . After completing his MS studies under the supervision of Prof. Osman Teoman Turgut, he moved to University of Illinois at Urbana-Champaign to study experimental biophysics. During his PhD studies with Professor Paul Selvin, he developed several single molecule imaging techniques for studying the stepping mechanisms of molecular motors called myosin V, myosin VI, and kinesin. In 2008, he started his postdoctoral studies at the Kishony Lab at Harvard Medical School where he developed novel technologies for studying bacterial drug resistance. He is an assistant professor and a research group leader at Sabanci University since 2011.