Unique Genetic Signature Predicts Drug Resistance in Bacteria

Antibiotic resistance represents a significant global health threat, responsible for over a million deaths each year. By 2050, the World Health Organization predicts that it could surpass cancer and heart disease as the leading cause of death as bacteria evolve new defenses against the drugs meant to treat them. Resistance occurs when bacteria are exposed to antibiotics that are ineffective at killing them, highlighting the importance of selecting the right treatment course. In a study published in Nature Communications, researchers have discovered a unique genetic signature in bacteria that can predict their likelihood of developing resistance to antibiotics. These findings could allow for faster identification of targeted treatments that are more effective against these dangerous, drug-resistant pathogens.

The study, conducted by researchers from Tulane University (New Orleans, LA, USA) and Informuta, Inc. (San Diego, CA, USA), focuses on Pseudomonas aeruginosa, a bacterium known for its multidrug resistance and frequent role in hospital-acquired infections. This bacterium often experiences deficiencies in a particular DNA repair pathway, a condition that accelerates mutations and increases the likelihood of antibiotic resistance. By analyzing bacterial genomes for mutational signatures, a technique commonly used in cancer research to track genetic changes in tumors, the team identified a distinct pattern associated with these repair deficiencies that accurately predicted the bacteria's potential to become resistant to antibiotics.

Worsening the situation, the study found that bacteria can acquire resistance to drugs that were not part of the initial treatment. Fortunately, the same DNA sequencing technology used to detect bacterial "fingerprints" can also pinpoint potential targets for treatment. The researchers were successful in identifying distinct resistance pathways and using specific antibiotic combinations to target these pathways, preventing the bacteria from becoming resistant. Although these findings are still in the early stages, the development of a diagnostic tool could help reduce the misuse of antibiotics and lead to more precise treatments for bacterial infections. Moving forward, Informuta plans to develop a machine learning model that can analyze bacterial samples and predict the likelihood of antibiotic resistance emerging.

“There’s absolutely nothing like this available right now, and it would be game changing for so many patient populations. Antibiotic resistance is getting worse year over year,” said lead author Kalen Hall, PhD, CEO and cofounder of Informuta. “I believe proper antibiotic stewardship and accurate diagnostics are important pieces of the puzzle.”

Related Links:
Tulane University
Informuta, Inc.

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