AI model reveals how genetic similarity drives antibiotic resistance in bacteria
The findings highlight the importance of understanding how genetic similarities between bacteria can impact their ability to become resistant to antibiotics. These insights could help in developing more effective strategies to prevent the spread of antibiotic resistance. By analyzing genetic data, researchers may be able to predict and monitor the emergence of antibiotic-resistant strains in various environments. This research underscores the need for continued efforts to address antibiotic resistance as a global health threat.
Introduction
April 2, 2025 This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: fact-checked peer-reviewed publication, trusted source, proofread by Chalmers University of Technology.
AI Model Predicts Antibiotic Resistance
An AI model trained on large amounts of genetic data can predict whether bacteria will become antibiotic-resistant. The new study shows that antibiotic resistance is more easily transmitted between genetically similar bacteria and mainly occurs in wastewater treatment plants and inside the human body.
"By understanding how resistance in bacteria arises, we can better combat its spread. This is crucial to protect public health and the healthcare system's ability to treat infections," says Erik Kristiansson, Professor at the Department of Mathematical Sciences at Chalmers University of Technology and the University of Gothenburg in Sweden.
Global Health Threat
Antibiotic resistance is one of the biggest threats to global health, according to the World Health Organization (WHO). When bacteria become resistant, the effect of antibiotics disappears, making conditions such as pneumonia and blood poisoning difficult or impossible to treat. Increased antibiotic-resistant bacteria also make it more difficult to prevent infections associated with many medical procedures, such as organ transplantation and cancer treatment.
Research Findings
A fundamental reason for the rapid spread of antibiotic resistance is bacteria's ability to exchange genes, including the genes that make the bacteria resistant. Research examines this complex evolutionary process to learn how these genes are transferred to pathogenic bacteria, making it possible to predict how future bacteria develop resistance.
Study Details
In the study, "Genetic compatibility and ecological connectivity drive the dissemination of antibiotic resistance genes," published in Nature Communications and conducted by researchers at Chalmers University of Technology, the University of Gothenburg, and the Fraunhofer-Chalmers Center, an AI model was developed to analyze historical gene transfers between bacteria using information about the bacteria's DNA, structure, and habitat.
HONESTAI ANALYSIS
The researchers hope that in the future, the AI model can be used in systems to quickly identify whether a new resistance gene is at risk of being transferred to pathogenic bacteria and translate this into practical measures. AI models could be used to improve molecular diagnostics to find new forms of multi-resistant bacteria or for monitoring wastewater treatment plants and environments where antibiotics are present.