System to auto-detect new variants will inform better response to future infectious disease outbreaks

jg533 — Wed, 01 Jan 2025 16:00:35 +0000

̽��ֱ��new approach uses samples from infected humans to allow real-time monitoring of pathogens circulating in human populations, and enable vaccine-evading bugs to be quickly and automatically identified. This could inform the development of vaccines that are more effective in preventing disease.

̽��ֱ��approach can also quickly detect emerging variants with resistance to antibiotics. This could inform the choice of treatment for people who become infected – and try to limit the spread of the disease.

It uses genetic sequencing data to provide information on the genetic changes underlying the emergence of new variants. This is important to help understand why different variants spread differently in human populations.

There are very few systems in place to keep watch for emerging variants of infectious diseases, apart from the established COVID and influenza surveillance programmes. ̽��ֱ��technique is a major advance on the existing approach to these diseases, which has relied on groups of experts to decide when a circulating bacteria or virus has changed enough to be designated a new variant.

By creating ‘family trees’, the new approach identifies new variants automatically based on how much a pathogen has changed genetically, and how easily it spreads in the human population – removing the need to convene experts to do this.

It can be used for a broad range of viruses and bacteria and only a small number of samples, taken from infected people, are needed to reveal the variants circulating in a population. This makes it particularly valuable for resource-poor settings.

̽��ֱ��report was published in the journal Nature.

“Our new method provides a way to show, surprisingly quickly, whether there are new transmissible variants of pathogens circulating in populations – and it can be used for a huge range of bacteria and viruses,” said Dr Noémie Lefrancq, first author of the report, who carried out the work at the ̽��ֱ�� of Cambridge’s Department of Genetics.

Lefrancq, who is now based at ETH Zurich, added: “We can even use it to start predicting how new variants are going to take over, which means decisions can quickly be made about how to respond.”

“Our method provides a completely objective way of spotting new strains of disease-causing bugs, by analysing their genetics and how they’re spreading in the population. This means we can rapidly and effectively spot the emergence of new highly transmissible strains,” said Professor Julian Parkhill, a researcher in the ̽��ֱ�� of Cambridge’s Department of Veterinary Medicine who was involved in the study.

Testing the technique

̽��ֱ��researchers used their new technique to analyse samples of Bordetella pertussis, the bacteria that causes whooping cough. Many countries are currently experiencing their worst whooping cough outbreaks of the last 25 years. It immediately identified 3 new variants circulating in the population that had been previously undetected.

“ ̽��ֱ��novel method proves very timely for the agent of whooping cough, which warrants reinforced surveillance given its current comeback in many countries and the worrying emergence of antimicrobial resistant lineages,” said Professor Sylvain Brisse, Head of the National Reference Center for whooping cough at Institut Pasteur, who provided bioresources and expertise on Bordetella pertussis genomic analyses and epidemiology.

In a second test, they analysed samples of Mycobacterium tuberculosis, the bacteria that causes Tuberculosis. It showed that 2 variants with resistance to antibiotics are spreading.

“ ̽��ֱ��approach will quickly show which variants of a pathogen are most worrying in terms of the potential to make people ill. This means a vaccine can be specifically targeted against these variants, to make it as effective as possible,” said Professor Henrik Salje in the ̽��ֱ�� of Cambridge’s Department of Genetics, senior author of the report.

He added: “If we see a rapid expansion of an antibiotic-resistant variant, then we could change the antibiotic that’s being prescribed to people infected by it, to try and limit the spread of that variant.”

̽��ֱ��researchers say this work is an important piece in the larger jigsaw of any public health response to infectious disease.

A constant threat

Bacteria and viruses that cause disease are constantly evolving to be better and faster at spreading between us. During the COVID pandemic, this led to the emergence of new strains: the original Wuhan strain spread rapidly but was later overtaken by other variants, including Omicron, which evolved from the original and were better at spreading. Underlying this evolution are changes in the genetic make-up of the pathogens.

Pathogens evolve through genetic changes that make them better at spreading. Scientists are particularly worried about genetic changes that allow pathogens to evade our immune system and cause disease despite us being vaccinated against them.

“This work has the potential to become an integral part of infectious disease surveillance systems around the world, and the insights it provides could completely change the way governments respond,” said Salje.

̽��ֱ��research was primarily funded by the European Research Council.

Reference: Lefrancq, N et al: ‘Learning the fitness dynamics of pathogens from phylogenies.’ January 2025, DOI: 10.1038/s41586-024-08309-9

Researchers have come up with a new way to identify more infectious variants of viruses or bacteria that start spreading in humans – including those causing flu, COVID, whooping cough and tuberculosis.

̽��ֱ��approach will quickly show which variants of a pathogen are most worrying in terms of the potential to make people ill. This means a vaccine can be specifically targeted against these variants, to make it as effective as possible.

Henrik Salje

Milan Krasula on Getty

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System to auto-detect new variants will inform better response to future infectious disease outbreaks

Testing the technique

A constant threat