Researchers discover bacterial DNA’s recipe for success

Duke University biomedical engineers have developed a new way to model how potentially beneficial packets of DNA called plasmids can circulate and accumulate through a complex environment that includes many bacterial species. The work also allowed the team to develop a new factor called ‘persistence potential’ which, when measured and calculated, can predict whether a plasmid will continue to thrive in a given population or gradually fade into oblivion.

The researchers hope their new model will lay the groundwork for others to better model and predict how important traits such as antibiotic resistance in pathogens or metabolic abilities in bacteria raised to clean up environmental pollution will spread and grow in a given environment.

The results appear online November 4 in the journal Nature Communications.

In addition to the Darwinian process of passing on genes important for survival from parents to offspring, bacteria also engage in a process called horizontal gene transfer. Bacteria are constantly sharing genetic recipes for new skills between species by swapping different packages of genetic material called plasmids with each other.

“In an examination of a single bottle of seawater, there were 160 bacterial species exchanging 180 different plasmids,” said Lingchong You, professor of biomedical engineering at Duke. “Even in a single bottle of water, using current methods to model plasmid mobility would far exceed the collective computing power of the entire world. We have developed a system that simplifies the model while maintaining its ability to predict with precision the final results “.

The potential of each of these genetic packages to become common in a given population or environment, however, is far from certain. It depends on a wide range of variables, such as how quickly packets are shared, how long the bacteria survive, how beneficial the new DNA is, what the trade-offs are for these benefits, and much more.

Being able to predict the fate of such a genetic package could help many fields, perhaps most notably the spread of antibiotic resistance and how to combat it. But the models needed to do this in a realistic scenario are too complicated to solve.

“The most complex system we’ve ever been able to mathematically model is three species of bacteria that share three plasmids,” You said. “And even then, we had to use a computer program just to generate the equations, because otherwise we would have been too confused with the number of terms needed. “

In the new study, you and your graduate student, Teng Wang, have created a new structure that greatly reduces the complexity of the model as more species and plasmids are added. In the traditional approach, each population is divided into multiple subpopulations based on the plasmids transported. But in the new system, these subpopulations are instead mediated into a single one. This dramatically reduces the number of variables, which increases linearly as new bacteria and plasmids are added rather than exponentially.

This new approach allowed researchers to derive a single governance criterion that allows them to predict whether or not a plasmid will persist in a given population. It is based on five important variables: the cost to bacteria of having the new DNA, how often the DNA is lost, how quickly the population is diluted by the flow through the population, how quickly the DNA is exchanged between the bacteria and how quickly the population as a whole is growing.

With measurements for these variables in hand, researchers can calculate the “plasmid persistence” of the population. If that number is greater than one, the genetic package will survive and spread, with higher numbers leading to greater abundance. If less than one, it will fade into oblivion.

“Even though the model is simplified, we found that it is reasonably accurate under certain limits,” Wang said. “As long as the new DNA doesn’t place too much load on the bacteria, our new framework will be successful.”

You and Wang tested their new modeling approach by designing a handful of different synthetic communities, each with different strains of bacteria and genetic packages for exchange. After running the experiments, they found that the results fit quite well with the expectations of their theoretical framework. And to go the extra mile, the researchers also took data from 13 previously published articles and also analyzed their numbers. These findings also supported their new model.

“The plasmid persistence criterion gives us hope to use it to drive new applications,” You said. control to eliminate or suppress certain plasmids from bacterial populations, such as those responsible for antibiotic resistance. ”