The problem of antibiotic-resistant bacteria has many health experts worried. As disease-causing bacteria adapt to some of our ways to reduce them, especially with antibiotics, it presents an arms race which we appear to be losing. Researchers seek to improve the situation by looking at how bacteria adapt to antibiotics. For the first time, researchers including those from the University of Tokyo, through massive data analysis, discovered two different strategies bacterial plasmids may use to share their antimicrobial resistance with other bacteria. Computational analysis could help identify and forecast risks, and future lab experiments may aid researchers narrow down key areas for further investigation.

Bacteria, including disease-causing ones, can evolve very quickly. One reason for this is their ability to share genetic material between themselves, leading to rapid adaptation. This sharing of genetic material is carried out using small loops of DNA ejected from bacteria called plasmids. Some plasmids can spread across a wide range of bacteria, while others are limited to a few. And as they commonly carry the genes for antimicrobial resistance, understanding how plasmids spread is a critical area of research.

“Our latest work shows that plasmids follow two very different survival strategies: Some behave stealthily, keeping their genes mostly silent to minimize their impacts, while others act in a manipulative way, actively interfering with the host’s systems to secure their own survival,” said Ryuichi Ono, an undergraduate student from the Department of Bioinformatics and Systems Biology at the time. “By analyzing more than 10,000 plasmid sequences, we discovered a clear pattern. Stealth plasmids tend to pick up new antibiotic-resistance genes first, and manipulative plasmids then help those genes spread rapidly. We dub this process ‘stealth-first,’ and it may help us anticipate how future resistance threats will emerge and spread.”

Ono and his team analyzed a range of bacteria from a group called Enterobacterales, which includes E. coli. They discovered that the two strategies, stealth and manipulation, rarely appear together in the same plasmid, and that plasmids using either strategy tend to carry more antibiotic-resistance genes than plasmids using neither.

“We identified specific genes, hns for stealthy plasmids, psiB for manipulative ones, that coincide with the less selective plasmids from the more selective ones respectively. Using these genetic signatures, we introduced the idea of ‘plasmid survival strategies’ as a new framework for understanding how plasmids evolve,” said Ono. “When we applied this framework to 48 major antibiotic-resistance genes, a consistent pattern appeared. This structure may explain past resistance outbreaks and help predict future ones.”

This research offers a new model for understanding plasmid evolution and helps in understanding the connection between the contents of plasmids with their potential targets. The team hopes this could lead to better tracking and prediction of new antibiotic-resistant infections, as the research reveals some likely vectors that can be detected and measured. And the implications of two genes, hns and psiB, being key components to this, could be useful targets for research into preventing the spread of antimicrobial resistance. However, it should be noted that this study focused on a single bacterial group and was based on computational analyses. Future investigations across additional bacterial lineages, together with experimental validation, will be important to facilitate further findings.

“Our stealth-first model suggests we may be able to anticipate future resistance threats by monitoring which genes appear mainly on stealth plasmids. If a resistance gene is still confined to stealth plasmids and has not yet moved on to manipulative plasmids, it may be on the verge of entering a rapid second phase of spread,” said Naoki Konno, a graduate student at the Department of Biological Sciences at the time. “We are especially excited about the potential to use this framework as an early warning system. Many groups worldwide are trying to predict how resistance genes move through bacterial populations, and we hope our work provides a practical step toward that goal. It’ll be a challenge to get there, but it’s one worth facing.”