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Drug resistance in E. coli: 'Baby steps' can pay off big

Date:
January 9, 2013
Source:
Rice University
Summary:
Scientists have found that mutations of seemingly small consequence 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 would allow E. coli to ward off attacks by the antibiotic minocycline.

Researchers examined the structure and performance of seven forms of a protein called TetX2 that had evolved to allow E. coli to resist attacks by the antibiotic minocycline. This image shows how a molecule of the drug, shown in yellow and gray, fits within the active site of a TetX2 protein.
Credit: Image courtesy of Rice University

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.

"Using the car analogy again, it's like we took one car and measured its top speed and acceleration using seven different engines," Shamoo said. "Then, we took the same car with an unknown engine and showed that we didn't have to open the hood and look to tell what kind of engine it had. We could tell that strictly by knowing its top speed and acceleration." By knowing how the car with the unknown engine performed on the race track, we could describe the engine without ever popping the hood and vice versa.

Shamoo said the research could lead to faster screening methods for resistant strains of bacteria, but the most immediate benefit is an improved understanding of how resistance develops.

"An example of that is this finding about the small steps," he said. "That was unexpected, and it tells us something fundamental about how resistance evolves. We still know that big changes can confer big benefits, but now we know that small changes can yield big benefits too. This tells us that we may need to look at things we might have overlooked in the past."

Co-authors include former graduate students Katarzyna Walkiewicz and Andres Benitez Cardenas, undergraduate senior Colin Barcorn, alumna Christine Sun and faculty fellow Gerda Saxer. The research was supported by the National Institutes of Health and the Kresge Science Initiative.


Story Source:

The above story is based on materials provided by Rice University. The original article was written by Jade Boyd. Note: Materials may be edited for content and length.


Journal Reference:

  1. K. Walkiewicz, A. S. Benitez Cardenas, C. Sun, C. Bacorn, G. Saxer, Y. Shamoo. Small changes in enzyme function can lead to surprisingly large fitness effects during adaptive evolution of antibiotic resistance. Proceedings of the National Academy of Sciences, 2012; 109 (52): 21408 DOI: 10.1073/pnas.1209335110

Cite This Page:

Rice University. "Drug resistance in E. coli: 'Baby steps' can pay off big." ScienceDaily. ScienceDaily, 9 January 2013. <www.sciencedaily.com/releases/2013/01/130109131546.htm>.
Rice University. (2013, January 9). Drug resistance in E. coli: 'Baby steps' can pay off big. ScienceDaily. Retrieved October 1, 2014 from www.sciencedaily.com/releases/2013/01/130109131546.htm
Rice University. "Drug resistance in E. coli: 'Baby steps' can pay off big." ScienceDaily. www.sciencedaily.com/releases/2013/01/130109131546.htm (accessed October 1, 2014).

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