Led by Dr Neil Keegan, Newcastle University
Co-Investigators: Prof Duncan Graham (University of Strathclyde), Prof Karen Faulds (University of Strathclyde), Prof Anil Wipat (Newcastle), Prof Molly Stevens (Imperial), Prof Calum McNeil (Newcastle), Prof Rachel McKendry (UCL).
Researcher Co-Investigators: Dr Keith Flanagan (Newcastle), Dr Chris Johnson (Newcastle)
Project partners: Cambridge Life Science Ltd, iXscient Ltd, and Public Health England
This four year collaborative project, led by Dr Neil Keegan, was awarded more than £1.3M funding and aims to build on the success of the i-sense IRC to develop a new type of rapid diagnostic system to detect and identify antimicrobial resistance (AMR).
Bacterial infection is an increasing problem, even in the developed world. Over the past 60 years antibiotics have been used to treat bacterial infections with good success. Treating a disease is much easier and cheaper if its presence can be detected early in the lifecycle. Recently, the effectiveness of antibiotics has begun to decline due the emergence of bacterial strains that are resistant to the commonly used antibiotics, indeed some are resistant to all current antibiotics. In order to effectively treat diseases caused by antibiotic resistant bacteria it is not enough to simply identify the bacterial species, it is also necessary to know whether the causative bacteria are resistant to the antibiotics that would usually be prescribed.
The novel bioinformatics system which was developed as part of the i-sense IRC (IDRIS) will be modified such that it will be capable of identifying the genetic features in bacteria that encode antibiotic resistance traits. This will be performed by; searching through genomic sequences, integrating distributed data and employing machine learning based techniques. The system will identify both previously known features and will also help characterise the evolution of new resistance genes. Hence the IDRIS system will generate the sequence signatures necessary for the production of new diagnostic technologies capable of identifying AMR.
In the main, molecular diagnostic technologies utilised to identify AMR genes require a nucleic acid pre‑amplification step, however, in u-Sense the team will detect target DNA using a method known as Surface-Enhanced Raman Scattering (SERS). SERS has the potential to detect rapidly and simultaneously, in a multiplexed format, a number of potential DNA sequences which are responsible for conferring resistance. Thus, SERS will push the limits of analytical sensitivity thereby reducing the requirement for pre‑amplification.
Ultimately, u-Sense will produce a miniaturised, cost-effective, rapid device which will allow clinicians to make informed treatment choices earlier. The developed system will be suitable for use at the Point-of-Need, outside of a centralised laboratory, in a variety of clinical settings in both developed and developing countries thereby promising a major impact on human health and disease management.