Among bacteria, only the «preppers» survive antibiotic treatment

What is the time delay between when a bacterium mutates, and when the mutation actually becomes useful? This question was once asked by the founding fathers of bacterial genetics, Luria and Delbrück in 1943. They expected this so-called “phenotypic delay” could have important implications for bacterial evolution, and for their Nobel Prize-winning work on bacterial mutation rates. Lacking a detailed understanding of bacterial physiology and the genetic tools to measure the phenotypic delay, this question became essentially forgotten.

Old question re-examined

In a recent study published in PLoS Biology, researchers from the Theoretical Biology and Molecular Microbial Ecology groups at D-USYS revisited and provided definite answers to this question. The study used a combination of experimental methods and mathematical modeling to show that phenotypic delay in bacteria can be as long as 3-4 generations and as a consequence, the conventional methods fail to accurately estimate mutation rates in bacteria. Furthermore, since mutations take a long time to express their phenotype and allow resistance to antibiotics, it means that those bacteria that survive antibiotic treatment have most likely acquired the necessary mutations in advance, like bacterial «preppers»  in a world of antibiotic stress.

On the trail of resistance

There is a long-held debate topic in antibiotic research: when we observe resistance mutations in clinical settings, where do they come from? Some suggest that resistances already exist, antibiotics only select for pre-existing mutants; others suggest that it’s the antibiotic treatment that turns normal bacteria into resistant mutants by stressing them. Given that mutations are not immediately useful for bacteria, this study suggests that pre-existing mutations are the more likely source.

According to the World Health Organization (WHO), resistance is an increasingly serious threat to global public health that could kill 10 million people each year by 2050. Understanding the process of resistance evolution will play key roles in controlling and reducing the resistance mutations.