Biophysicists discover how small populations of bacteria survive treatment
By Carol Clark | eScienceCommons | March 13, 2018
"We showed that by tuning the growth and death rate of bacterial cells, you can clear small populations of even antibiotic-resistant bacteria using low antibiotic concentrations," says biophysicist Minsu Kim. His lab conducted experiments with E. coli bacteria.
Small populations of pathogenic bacteria may be harder to kill off than larger populations because they respond differently to antibiotics, a new study by Emory University finds.
The journal eLife published the research, showing that a population of bacteria containing 100 cells or less responds to antibiotics randomly — not homogeneously like a larger population.
“We’ve shown that there may be nothing special about bacterial cells that aren’t killed by drug therapy — they survive by random chance,” says senior author Minsu Kim, an assistant professor in the Department of Physics and a member of Emory’s Antibiotic Resistance Center.
“This randomness is a double-edged sword,” Kim adds. “On the surface, it makes it more difficult to predict a treatment outcome. But we found a way to manipulate this inherent randomness in a way that clears a small population of bacteria with 100 percent probability. By tuning the growth and death rate of bacterial cells, you can clear small populations of even antibiotic-resistant bacteria using low antibiotic concentrations.”
Jessica Coates, as a graduate student at Emory, and Bo Ryoung Park, a research associate in the Kim lab, are co-first authors of the paper. Additional authors are graduate student Emrah Simsek and post-doctoral fellows Dai Le and Waqas Chaudry.
The researchers developed a treatment model using a cocktail of two different classes of antibiotic drugs. They first demonstrated the effectiveness of the model in laboratory experiments on a small population of E. coli bacteria without antibiotic-drug resistance. In later experiments, they found that the model also worked on a small population of clinically-isolated antibiotic-resistant E. coli.
“We hope that our model can help in the development of more sophisticated antibiotic drug protocols — making them more effective at lower doses for some infections,” Kim says. “It’s important because if you treat a bacterial infection and fail to kill it entirely, that can contribute to antibiotic resistance.”