A groundbreaking study led by Dr. Noémie Lefrancq from the University of Cambridge, published in the journal Nature on January 2, 2025, introduces a new method for identifying infectious variants of viruses and bacteria.

This innovative technique has been tested on various pathogens, including Bordetella pertussis and Mycobacterium tuberculosis, uncovering previously undetected variants during outbreaks.

Specifically, the method revealed three new variants of Bordetella pertussis amidst a whooping cough outbreak and two antibiotic-resistant variants of Mycobacterium tuberculosis that are currently spreading.

By detecting antibiotic-resistant variants, this method aids in treatment decisions for infected individuals, thereby limiting the spread of diseases.

The approach constructs 'family trees' for pathogens, automatically identifying new variants based on genetic changes and transmissibility, which streamlines the classification process.

Utilizing genetic sequencing data, the method enhances understanding of the genetic changes that contribute to the emergence of new variants, improving surveillance capabilities.

Current surveillance systems are primarily focused on COVID-19 and influenza, making this technique a significant advancement in monitoring a wider range of infectious diseases.

Researchers emphasize the potential of this method to enhance global infectious disease surveillance and inform effective public health responses.

The technique is applicable to a wide range of pathogens and requires only a small number of samples, making it particularly useful in resource-limited settings.

By enabling real-time monitoring of pathogens from human samples, the method allows for the quick identification of vaccine-evading strains.

The insights gained from this research could transform governmental responses to infectious disease outbreaks worldwide.

The ongoing evolution of pathogens, highlighted by the emergence of new strains during the COVID-19 pandemic, underscores the necessity for robust surveillance methods.