Antibiotic resistance is a global public health problem. The straightforward solution to this problem is developing new antibiotics that can kill all of the drug resistant bugs, alas; this has not been possible so far due to economic and natural limitations. Another plausible solution to this problem is the effective use of already existing antibiotics by designing novel treatment strategies. However, efforts towards finding such strategies have not been rewarding to the date due to our limited knowledge about the origins of antibiotic resistance at the molecular and population levels. In order to tackle this problem, we performed an extensive laboratory evolution experiment where we evolved drug sensitive E.coli populations against 22 different clinically important antibiotic compounds and systematically phenotyped and genotyped evolved populations. Benefiting from this extensive data set, we identified common genetic targets for resistance conferring mutations and resulting phenotypic changes. Our analysis allows us design effective multidrug treatments strategies that can slow down evolution of antibiotic resistance. We hope that, the methodologies that were developed throughout this study will also be helpful for finding effective therapies for combating cancer and immune disease
