Drug resistance: 'Baby steps' can pay off big

Rice University scientists have found that mutations of small effect can turn out to be game changers in the bacterial fight against antibiotic drugs.

The discovery came during an exhaustive, three-year effort to create a mathematical model that could accurately predict how specific mutations allow bacteria like E. coli to adapt to antibiotics like minocycline. The findings are detailed in a Dec. 10 study in the Proceedings of the National Academy of Sciences.

"As biologists, we tend to focus on big effects that result from big changes, but this study shows that bacteria don't have to solve the problem of antibiotic resistance in one giant step," said Rice University biochemist Yousif Shamoo, the lead researcher on a new study. "We were surprised to see that small mutations could make a big difference. In some cases, we saw that minor molecular changes could boost resistance by as much as 500 percent."

Despite the remarkable success of antibiotics, bacterial infections remain a leading cause of death, and antibiotic resistance among bacterial pathogens is a significant threat to public health.

Shamoo, professor of biochemistry and cell biology and director of Rice's Institute of Biosciences and Bioengineering, said the study of antibiotic resistance offers opportunities to both examine complex cellular phenomena and address the "genotype to phenotype problem," one of modern biology's major challenges.

"We've sequenced hundreds of genomes, and we're finding that it's one thing to read the blueprint, and it's another thing to predict how the DNA in that blueprint will affect change in a living cell," Shamoo said.

Shamoo said that because a single antibiotic resistance gene can make big changes in whether a cell lives or dies, it is possible to make the connection between the gene and the cell's fitness to its environment.

To show that a mathematical model could accurately predict how specific mutations would affect E. coli resistance, Shamoo's team examined a gene called TetX2. The gene encodes an enzyme that inactivates the antibiotic minocycline. The researchers looked at the most likely mutations of the gene and found seven that led to increased antibiotic resistance. They then spent two years amassing a catalog of basic biochemical measurements for each mutant.

"If this organism were a car, then these enzymatic measurements would be the equivalent of things like the cubic displacement of the engine, the torque and so on," Shamoo said. "These are measurable variables, but by themselves they won't tell you the car's top speed or how fast it can accelerate from 0 to 60 (miles per hour)."

By measuring the performance improvements from each of the seven TetX2 mutants, the group was able to build a mathematical formula that accurately correlated the E. coli resistance with enzyme performance. To test the formula, the team measured the resistance of a new family of mutants and used the formula to calculate their enzyme performance metrics.