16 Aug 2023

Scientists hail ‘game changer’ DNA technique for infection ID

Scientists from The University of Edinburgh claimed their method, which applies DNA extraction and sequencing techniques, can identify bacterial presence within a few hours.

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Allister Webb

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Scientists hail ‘game changer’ DNA technique for infection ID

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A new rapid diagnosis method for canine bacterial infections has been hailed as a potential “game changer”, with the possibility of offering both faster treatments and reductions in antimicrobial resistance (AMR).

Scientists from The University of Edinburgh claimed their method, which applies DNA extraction and sequencing techniques, can identify bacterial presence within a few hours.

The approach has been outlined in a new paper, published in the journal Microbial Genomics, and the researchers believe its effects could be far reaching for both animals and humans.

New standard

Senior clinical researcher Natalie Ring said: “We think it’s going to be a game changer. In 10 or 20 years’ time, this is going to be standard.

“Everybody is going to be doing this.”

So far, the process has been tested solely on urinary tract and skin infections within dogs; although, the research team believes it can be applied and adapted to a range of species, including humans, and different samples such as blood, as well as viruses and other potential sources of infection.

Grant applications are being drawn up to continue the research in different species areas, while medics at a London hospital are also understood to be examining potential uses for human treatments.

A new lab is also being developed for the project and work there is expected to begin this autumn.

Nanapore sequencing

The method uses metagenomic DNA extraction to collect all the material from a sample – regardless of species – along with nanopore sequencing, which can generate DNA code from samples, and a data analysis tool that identifies bacteria from DNA.

Although further work is understood to be needed in relation to skin conditions, the research found that the process identified bacteria within 5 hours, compared to the 48 to 72 hours it can take for current techniques to yield similar results.

It also found the system could identify bacterial species that can be difficult to observe through culturing and indicate whether the identified species might be resistant to antibiotics, and predicted antibiotic sensitivity with up to 95% accuracy in urine samples.

The researchers argued this will enable vets to prescribe antibiotics that are appropriate for the condition more quickly, rather than spending time and money on ineffective treatments.

That point is also critical amid the sustained efforts to tackle the issue of AMR – particularly in the farming industry – in recent years.

‘Limit resistance’

The paper argues the process will help to address what it describes as the “urgent need to ensure that antimicrobials are used appropriately to limit the emergence and impact of resistance” by reducing the use of broad-spectrum antibiotics, which can be ineffective and increase resistance.

Dr Ring said the process, which is intended to be applicable in settings ranging from small practices to large hospitals, has already attracted industry interest, but is likely to be more expensive for practices to use than current methods.

However, she believes that the one health potential of the method will encourage professionals to use it.

She added: “We are confident that this approach has potential for use across many animal species, and in humans, and has applications in other infection types.

“It could play a significant role in enabling responsible use of antimicrobial treatments and limiting antimicrobial resistance.”

Grant funding

The research has been funded by Dogs Trust through its Canine Welfare Grant programme.

Paula Boyden, the charity’s veterinary director, said it was “delighted” to support the study.

She added: “This will allow for much quicker treatment of the canine patient and, consequently, a potential reduction in recovery time.”

The research has also been supported by the Data-Driven Innovation programme, which is part of the Edinburgh and South East Scotland City Region Deal, and funded by both the UK Government and Scottish Government.