Gene Expression Key for Infection Diagnosis

A test using a single drop of blood to detect and distinguish between 18 infectious or inflammatory diseases – including group B Streptococcus (GBS), respiratory syncytial virus (RSV) and tuberculosis – has been developed and validated by an international research team.

The preliminary findings built on more than a decade of research to detect and diagnose illnesses based on patterns of gene expression, which led to the establishment of the DIAMONDS consortium in 2020 - an international project funded by the EU Horizon2020 programme and led by Imperial College London to develop rapid diagnostic tests for febrile illnesses. 

Professor Michael Levin, Chair in Paediatrics & International Child Health within the Department of Infectious Disease at Imperial College London and co-senior author of the paper, explained that children brought in with a fever are treated initially based on the doctors’ ‘impression’ of the likely causes of the illness, including symptoms, information from the parents and medical training and experience.

 “As clinicians, we need to make rapid decisions on treatment, but we may not know whether a fever is bacterial, viral, or something else until hours or days after a child has been admitted, when their test results come back. Such delays can stop patients getting the right treatment early on, so there is a clear and urgent need to improve diagnostics. Using this new approach, once it’s translated to near point of care devices, could be transformative for healthcare.” 

The study used data from thousands of patients including more than 1000 children with 18 infectious or inflammatory diseases, to identify which key genes were switched ‘on’ or ‘off’ in response to a range of illnesses. Machine learning was then applied to identify which patterns of gene expression corresponded to the specific disease areas and pathogens - focusing in on a panel 161 genes for 18 conditions.

Dr Myrsini Kaforou, Senior Lecturer within Imperial’s Department of Infectious Disease and co-senior author of the paper, said: “This body of work has enabled us to identify the molecular signature of a wide range of diseases based on 161 genes, out of thousands of genes in the human genome. By distinguishing between many diseases at the same time within the same test, we have developed a more comprehensive and accurate model that aligns with the way clinicians think about diagnosis.  

“With this initial proof-of-concept study, we’ve been able to show that our multi-disease machine-learning diagnostic approach works. This kind of advance is only possible through interdisciplinary collaboration and large research consortia, which bring together expertise from infectious disease, molecular science and bioinformatics,”

The researchers stressed that a functional test is not yet available for clinical practice and their RNA transcript panel would require further adaptation, testing and translation into a readily usable platform/device before it could be approved by regulators.  

As part of the DIAMONDS study, the next step would be to trial the approach in thousands of patients in hospitals in Europe, Africa and Asia.

'Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature’ by Habgood-Coote, D. Wilson, C. Shimizu, C. et al. Published in Cell Press Med. 

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