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Fields & Applications Proteomics

Proteomic Biomarkers: Predicting Death from Staphylococcus aureus Bacteremia

Staphylococcus aureus bacteremia is a major cause of illness and death worldwide – and one that’s tricky to diagnose early. The longer the infection goes untreated, the more a patient’s risk of death increases. With this in mind, David Gonzalez and colleagues – proteomics specialists at the University of California San Diego School of Medicine – investigated whether proteomic and metabolomic readouts from a patient’s blood sample could serve as predictive markers of response to infection – and, ultimately, risk of mortality.

“The faster we know what’s going to happen to our patients, the better we can treat them,” said George Sakoulas, a co-author of the study (1). Over the course of two years, the team worked to identify serum proteins and metabolites in patients’ blood that could predict those most at risk of death from S. aureus bacteremia (2). By analyzing over 10,000 proteins and metabolites in more than 200 serum samples, they found that patients who died of S. aureus bacteremia exhibited a different pattern of proteins to those who survived.

The biomarkers most associated with death were lower levels of glycosylated fetuin A, unmodified fetuin B, and thyroxine, and higher levels of serum protein carbamylation. But an unanswered question remained: Do the differences actively cause increased mortality? Using a mouse model to investigate thyroxine levels, the team administered either hypo- or hyperthyroid treatment to infected mice and monitored survival rates. Compared with the control group, hyperthyroid mice had a survival rate four times higher 48 hours after infection, whereas hypothyroid mice exhibited decreased survival – suggesting that thyroxine levels directly affect disease outcome.

“This finding is a leap forward toward a point-of-care predictive tool for bacteremia risk,” said Gonzalez (1). “It also opens up lots of new basic biological questions about how our immune systems respond to infections.”

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  1. UC San Diego Health (2020). Available at:
  2. JM Wozniak et al., Cell, 182, 1311 (2020). PMID: 32888495.
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