Genetic Signature May Predict Neonatal Sepsis Early: Study Reveals
A genetic signature in newborns can predict neonatal sepsis before symptoms even start to show, according to a new study. The study, led by UBC and SFU researchers in collaboration with the Medical Research Council (MRC) Unit The Gambia, has the potential to help healthcare workers diagnose babies earlier, including in lower- and middle-income countries (LMICs) where neonatal sepsis is of particular concern. The research was published in eBiomedicine.
Diagnosing sepsis is challenging for doctors and families. The symptoms can look like many other illnesses, and tests to check if sepsis is present can take several days, aren’t always accurate, and are largely only available in hospitals. The uncertainty can delay urgent treatment with antibiotics.
The researchers participated in a large study in The Gambia where blood samples were taken from 720 infants at birth. Out of this cohort, 15 babies developed early-onset sepsis. The researchers used machine learning to map the expression of genes active at birth, in search of biological markers that could predict sepsis.
“We found four genes that, when combined in a ‘signature’, could accurately predict sepsis in newborns nine times out of 10,” said co-senior author Dr. Amy Lee, assistant professor in the SFU Department of Molecular Biology and Biochemistry. “This was a unique opportunity where samples were available from all babies in this cohort on the day of birth, meaning we could study the genes expressed in the sepsis babies before they got sick. Most other studies have only published markers that were present when the babies were already ill, and this would therefore not be a predictive signature.” The researchers hope the signature will one day be incorporated not only into PCR tests in hospitals but also in portable, point-of-care devices.
Reference: Andy Y. An, Erica Acton, Olubukola T. Idoko, Casey P. Shannon, Travis M. Blimkie et al. Predictive gene expression signature diagnoses neonatal sepsis before clinical presentation, eBioMedicine, 2024, 105411, ISSN 2352-3964, https://doi.org/10.1016/j.ebiom.2024.105411.
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