Reducing the risk of bacteremia

New biomarkers can predict risk of patients dying from bacteremia

March 25, 2021
Mel J. Yeates
Reducing the risk of bacteremia

SAN DIEGO—One of the greatest challenges for clinicians involves the time it takes to diagnose a patient who has bacteremia. But a published study of new research offers the promise that we may be able to change this.

“The faster we know what’s going to happen to our patients, the better we can treat them,” said Dr. George Sakoulas, an infectious disease specialist and associate adjunct professor of pediatrics at University of California, San Diego (UC San Diego) School of Medicine, as well as a co-author of the study.

“We tend to treat all bacteremia patients with the same cheap antibiotics, yet we know they only work for 80 percent of these patients. We need to know from the beginning who falls into that 20 percent that will require a more complex treatment regimen so we don’t waste time with trial-and-error,” he added.

In a study published in Cell, researchers at UC San Diego have described one of the most comprehensive molecular assessments of blood serum from any human infection response to date. They were also able to validate their findings in mouse models of Staphylococcus aureus bacteremia.

Dr. David Gonzalez, a biochemist who specializes in proteomics, and his colleagues have identified a collective signature of proteins and metabolites associated with death due to S. aureus bacteremia. These biomarkers can predict who is at highest risk of dying from the infection with exceptional accuracy.

“This finding is a leap forward toward a point-of-care predictive tool for bacteremia risk. It also opens up lots of new basic biological questions about how our immune systems respond to infections,” points out Gonzalez, assistant professor at UC San Diego School of Medicine and the Skaggs School of Pharmacy and Pharmaceutical Sciences, and senior author of the study. Gonzalez led the study with first author Dr. Jacob Wozniak, at the time a graduate student in Gonzalez’s lab.

The team used mass spectrometry to analyze more than 10,000 proteins and metabolites in more than 200 serum samples collected from the blood of patients with S. aureus bacteremia. Serum is notoriously difficult to study, since it’s heavily laden with highly abundant serum proteins.

“So, at first, the depth of our proteomic data was a total letdown. We didn’t learn as much as we had hoped about the serum proteins,” Gonzalez reported.

This initial hurdle inspired the team to look deeper at post-translational modifications. And with this approach, the team identified a specific pattern of proteins—with and without post-translational modifications—that differed in the serum of patients who ultimately died of S. aureus bacteremia, compared to patients who did not.

“In addition to defining standard protein and metabolite biomarkers for SaB [Staphylococcus aureus bacteremia] mortality, we utilized two computational strategies for a deeper analysis,” notes the article. “First, using a workflow for the prediction and identification of PTMs [post-translational modifications], we revealed that the paucity of identifications in the serum proteome likely derives from modified peptides, including both serum glycoproteins and small PTMs. Using refined database-searching techniques resulted in the identification of our top predictive biomarkers, glycosylated peptides derived from fetuin A.”

“The second computational advancement in serum bioanalytics utilized in this study is the inferring of cytokine signatures from serum proteomics data. This analysis predicted major alterations in IL-6, TGF-b1, TNF, IL-1b, and IL-10 in mortality samples, all of which were validated by an orthogonal approach (i.e., IPA) and have exhibited associations to SaB human mortality in previous studies (Guimaraes et al., 2019; Minejima et al., 2016; Rose et al., 2012, 2017),” the article continues. “This approach also enables researchers to link these major cytokine players to the observed proteomic data, facilitating the construction of testable hypotheses (such as the impact of adiponectin signaling in SaB). Together, this method refined host response pathway analysis and identified unreported potential players in SaB.”

The biomarkers most highly associated with death included lower levels of glycosylated fetuin A, unmodified fetuin B, and thyroxine—a master regulator of metabolism—as well as higher levels of serum protein carbamylation, another post-translational modification. The analyses revealed serum differences between low- and high-risk patients, but it wasn’t clear whether these molecules actually contribute to the disease, or if they were simply bystanders.

To elucidate the results, Gonzalez and his team used a mouse model of S. aureus bacteremia to explore cause and effect. They found that mice with higher thyroxine levels had a four-times greater survival rate at 48 hours post-infection than control mice. These results indicated that at least one of the identified biomarkers plays a direct role in disease outcome.

With this proteomics-based prediction method, researchers could predict who is most likely to die of S. aureus bacteremia with excellent predictability, with an AUC of 0.95.

In addition to following up on the proteins and modifications that were revealed in this study—exploring their origins, roles in immune response, and potential as therapeutic targets—the team is now working to translate their mass spectrometry observations in the laboratory into a rapid clinical test that uses antibody probes to detect S. aureus bacteremia-associated proteins. Researchers are also looking at proteomic and metabolomic markers indicative of high-risk patients with other types of infections, including COVID-19.

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