A new model based upon gene activity of immune cells can predict how the body’s immune system might respond to sepsis, COVID-19 and influenza, British scientists have reported.
This could help to explain why the immune response fails in some individuals, and the varying outcomes for these diseases, according to researchers.
Developed by researchers at the Wellcome Sanger Institute, the University of Oxford, Queen Mary University, Imperial College and their collaborators, the gene expression model is based upon 19 genes that indicate a dysfunctional immune response.
Writing in Science Translation Medicine, the team says the findings could open the door for applying precision medicine techniques to diseases such as sepsis, which are difficult to diagnose and treat.
Sepsis can arise from multiple causes, and there are no targeted treatments available for this highly variable disease. Positive results from some drug trials have not been reproducible in others.
The hypothesis that having a greater understanding of sepsis at the molecular level, which would enable patients to be classified according to the particular characteristics of their illness, is pivotal to identifying those at risk and developing effective treatments.
In this new study, the team from the Wellcome Sanger Institute and the University of Oxford set out to develop a gene expression model to help understand which sepsis patients are more likely to have particular responses and potentially poor outcomes.
The research included 1,655 samples from sepsis patients collected as part of the UK Genomic Advances in Sepsis study.
These samples were sequenced at the Wellcome Sanger Institute to assess gene expression – the activity of different genes – among immune cells in the blood. The data were combined with existing data from sepsis patients and healthy individuals.
The team’s analysis revealed patterns of gene expression that signalled an inappropriate immune response. This allowed them to predict clinical outcomes in sepsis, H1N1 influenza, and COVID-19, from a group of just 19 genes.
Study co-leader Professor Julian Knight, from the University of Oxford, said: “We urgently need better ways to understand what goes wrong with the immune system in response to infection to cause sepsis, a disease with devastating results for millions of people each year around the world.
“A fast, accurate test to predict who has a particular type of immune response to infection and is at greater risk from poorer outcomes in sepsis would help massively and now seems a genuine possibility.”
A machine-learning framework was developed to test it on sepsis, SARS-CoV-2 and H1N1 influenza. The model successfully predicted an individual’s likelihood of poor outcomes in these three diseases.
Study first author Dr Eddie Cano-Gamez, from the University of Oxford and Wellcome Sanger Institute, said: “Now that we have the ability to predict sepsis outcomes from just 19 genes, it’s crucial that as many researchers as possible can take advantage of this approach.”
He added that the team has created a code package for other researchers to run the model on their own data.
The research team now aim to study the underlying immune dysfunction involved in sepsis to develop biomarker-led clinical trials, which would help to target the most effective therapies at those who would benefit most.
Study co-leader Dr Emma Davenport, from the Wellcome Sanger Institute, said: “Sepsis has long seemed an intractable problem because we simply didn’t understand the disease as well as we needed to.
“Similarly, the early stages of the COVID-19 pandemic highlighted the stress doctors were under, trying to treat patients without having solid information to help them identify those most at risk. Our model provides a level of detail that finally allows us to start applying precision medicine techniques to sepsis and improve outcomes for patients.”
Cano-Gamez E, Burnham KL, Goh C, Allcock A, Malick ZH, Overend L, Kwok A, Smith DA, Hessel Peters-Sengers H, Antcliffe D, GAinS Investigators, McKechnie S, Scicluna BP, van der Poll T, Gordon AC, Hinds CJ, Davenport EE, Knight JC. (2022) “An immune dysfunction score for stratification of patients with acute infection based on whole blood gene expression.” Science Translational Medicine, doi: 10.1126/scitranslmed.abq4433
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