Feeding 9 Billion: Innovations in Agricultural Modeling
Spotting disease causing germs has just become a lot easier with a new technique developed by researchers from Newcastle upon Tyne, scientists learned today, Wednesday 10 September 2003, at the Society for General Microbiology’s meeting at UMIST in Manchester.
“Knowing exactly which bacteria and micro-organisms are around at any given time is vital if you want to be able to kill the dangerous ones,” says Dr Olivier Sparagano, from the School of Agriculture, Food and Rural Development at the University of Newcastle. “Any method of diagnosis has got to be able to detect several different species of minute organisms simultaneously, and warn when unexpected ones show up. We have developed a new molecular method of screening bacteria and other disease causing organisms.”
Some types of germs rely on luck, and cannot invade their hosts – which can be people or animals – unless other micro-organisms have already attacked the host’s defences first. But many different types of disease causing bacteria and other bugs can be around at the same time, so detection methods need to be able to spot them simultaneously. In addition, the commonest disease bearing arthropods such as ticks and mosquitoes can carry and transmit more than one infection at the same time. All these complex factors make choosing exactly the right combination of protection and control, such as vaccination or antibiotics, very difficult.
By concentrating on known differences in the genes and DNA sequences of infectious micro-organisms, the team from Newcastle, and their colleagues in Spain, Italy and Turkey, were able to build up a screening system, which can spot even previously unknown dangerous germs at an early stage of infection. This knowledge will in turn allow other scientists to target the genes needed for the disease causing bugs to survive, and block their action, preventing the organisms from spreading.
The development of this technique allows much more accuracy and sensitivity compared with previous methods of disease detection.
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