A team of researchers led by investigators at the University of California, San Diego (UCSD), have identified a collective signature of proteins and metabolites that lab tests indicate can predict with exceptional accuracy the likelihood of death resulting from bacteremia (SaB)—a bacterial infection in the blood that kills 20–30% of infected patients.
The researchers’ study, reported in Cell, involved what they claim is one of the most comprehensive molecular assessments of blood serum from any human infection response to date. The team validated their findings in a mouse model of S. aureus bacteremia, and are expanding their approach to look at proteomic and—markers that may be indicative of high-risk patients with other types of infections, including COVID-19.
“This finding is a leap forward toward a point-of-care predictive tool for bacteremia risk,” said David Gonzalez, Ph.D., assistant professor at UC San Diego School of Medicine and Skaggs School of Pharmacy and Pharmaceutical Sciences. “It also opens up lots of new basic biological questions about how our immune systems respond to infections.” The researchers are now working to translate their mass spectrometry observations from the laboratory, into a rapid clinical test that uses antibody probes to detect S. aureus bacteremia-associated proteins.
Gonzalez is one of the senior authors of the published paper, which is titled, “Mortality Risk Profiling of Staphylococcus aureus Bacteremia by Multi-omic Serum Analysis Reveals Early Predictive and Pathogenic Molecular Signatures.” Gonzalez led the study with first author Jacob Wozniak, PhD, a graduate student in his lab at the time.
Patients with SaB may display a range of disease severity and outcomes, the researchers noted. Some infected individuals clear the pathogen on receipt of first-line therapy, whereas others can’t overcome the infection. This heterogeneity of SaB can make it hard to select the best therapy, the authors noted. “The current standard of care is to administer broad-spectrum antibiotics while awaiting pathogen susceptibilities to guide treatment decisions,” they wrote. “However, blood cultures are not always attainable, and it may take several days to deduce antibiotic susceptibilities.” Delays in intervention can exacerbate patient mortality. “The ability to rapidly predict SaB patient responses and guide personalized treatment regimens could reduce mortality.”
Gonzalez’s “aha” moment came when a physician-colleague, George Sakoulas, MD, shared with him this major issue in clinical practice, i.e., how long it takes to diagnose a patient. “The faster we know what’s going to happen to our patients, the better we can treat them,” said co-author Sakoulas, an infectious disease specialist and associate adjunct professor of pediatrics at University of California San Diego School of Medicine. “We tend to treat all bacteremia patients with the same cheap antibiotics, yet we know they only work for 80% of these patients …We need to know from the beginning who falls into that 20% that will require a more complex treatment regimen, so we don’t waste time with trial-and-error.”
Gonzalez is a biochemist who specializes in proteomics, using mass spectrometry to identify proteins. He wondered whether a proteomic “readout” from a person’s blood could help to identify those patients who might need the most help early on, so that they could be treated quickly and appropriately?
For their newly reported study, Gonzalez and 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, he said, because it is heavily laden with a handful of highly abundant serum proteins. “So, at first, the depth of our proteomic data was a total let down,” Gonzalez said. “We didn’t learn as much as we had hoped about the serum proteins.”
This hurdle motivated the team to look deeper, at post-translational modifications. Many research efforts are geared toward genomics, but the gene that encodes a protein doesn’t reveal much about how it might subsequently be modified. And according to Gonzalez, post-translational modifications are mostly uncharted territory. “If I wanted to learn all about you, I’d just talk to you directly, not your second cousin,” Gonzalez said. “Same thing here, we can gain new and important information by directly ‘asking’ the proteins, rather than their genes, and mass spectrometry is currently the best way to do that in an unbiased manner.”
Taking this approach, the team identified a specific pattern of proteins with and without post-translational modifications that differed in the serum of those patients who ultimately died of S. aureus bacteremia, compared to those who did not. The biomarkers that were most highly associated with death included lower levels of glycosylated (sugar-coated) 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.
Several of these new biomarkers are already known to be associated with disease – for example, high fetuin levels are associated with obesity and diabetes, and carbamylation has been linked with kidney disease—however, few have been previously linked to bacterial infections.
While the analyses revealed serum differences between low- and high-risk patients, it wasn’t clear whether these molecules actually contributed to the disease, or were simply bystanders. So, the investigators next 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 after infection than control mice. These results indicated that at least one of the identified biomarkers plays a direct role in disease outcome.
Research groups have previously developed alternative methods for predicting a patient’s risk of death due to bacteremia. However, Gonzalez says, at best their accuracy has been only fair to good. In contrast, the researchers believe, using the proteomics-based prediction method, they could predict who is most likely to die of S. aureus bacteremia with excellent predictability. To put it quantitatively, the area under the curve (AUC) was 0.95; 1.0 is perfect and anything above 0.90 is considered excellent in this standard measure of the ability of a test to correctly classify those with and without the disease.
“Through a multi-omic approach, we define numerous features and multivariate models that can accurately predict SaB patient mortality,” the team concluded. “These features can be paired with previously described cytokine markers, quantified with more sensitive immunoassays, to enhance prognostic value … “Our findings represent a starting point for the development of a prognostic test for identifying high-risk patients at a time early enough to trigger intensive monitoring and interventions …. Ultimately, this study sets the groundwork for a multi-marker-based tool for the rapid prediction of SaB patient mortality at the time of clinical presentation: the Rapid Index of SaB Mortality Kinetics (RISK) test.”
The team suggested that carrying out future studies to the same depth and rigor will likely uncover additional clinically useful findings, which further improve what we understand about mortality in infection. The researchers are also following up on the proteins and modifications that were revealed in the study, exploring their origins, roles in the immune response and potential as therapeutic targets.
