Researchers may have uncovered the mechanisms behind fungal bloodstream infections in patients who receive bone marrow transplants, according to a recent study published by Zhai et al in Nature Medicine.
Background
A phenomenon known as heteroresistance occurs when a tiny fraction of bacteria remain resistant to antibiotics while the remainder succumb to the drugs. Patients receiving bone marrow transplants also face an equally deadly threat of fungal bloodstream infections—including infections from Candida parapsilosis, a species of fungi that can live in the digestive tract and occasionally make it into the bloodstream.
Previous studies have observed that a number of transplant recipients may develop bloodstream infections, despite being treated with the antifungal drug micafungin. Among the infected patients, there was found to be a high mortality rate; however, the mechanisms causing the infections were still little understood.
Study Methods and Results
In the recent study, the researchers gathered 219 strains of C parapsilosis from patients at Memorial Sloan Kettering Cancer Center and locations in France, Germany, and China.
The researchers discovered that heteroresistance was the driver of fungal bloodstream infections in a small number of patients who had received prophylaxis with micafungin.
“The fungi are just trying to survive, as we would do when faced with a threat,” explained co–study author David Weiss, PhD, Professor at the Emory University School of Medicine and Director of the Emory Antibiotic Resistance Center. “They divide within hours, so they have many, many more rounds of evolution. Every time we use antifungals, it's an opportunity for the fungi to go to school and learn how to survive. For them, surviving means they're resisting the drug and potentially causing a problem for us,” he noted.
The researchers then used a machine learning model to help detect the heteroresistant fungi, demonstrating a proof-of-principle diagnostic approach with the potential to inform clinical decision-making. They revealed complex patterns indicating that heteroresistant and susceptible strains were more likely to have slightly different evolutionary histories known as phylogenetic clusters. Although the machine learning model wasn’t strictly necessary to discover this, it helped predict heteroresistance based on only a small set of genomic features that can be rapidly measured by existing tools.
“There are thousands of mutations,” stressed co–study author Chen Liao, PhD, a computational biologist at Memorial Sloan Kettering Cancer Center. “I asked my algorithm to choose at most 10. One of the advantages of machine learning is that you don’t need to sequence the whole genome, just find a few spots that are informative enough that they can predict,” he added.
Conclusions
The researchers hope their innovation holds promise for developing a simple test to identify heteroresistant fungi in a clinical setting. Nonetheless, it could take years of research to determine the precise molecular mechanisms that cause heteroresistance.
“Right now, there’s no test for heteroresistance,” underscored Dr. Weiss. “What we should be striving for is to analyze a fecal sample from a patient prior to transplant in order to profile their gut microbes or fungi. If they have a micafungin-heteroresistant C parapsilosis, clinicians would be able to choose a different antifungal for prophylaxis or get rid of the fungus from the gut before doing the transplant. [We] don't want these fungi there because that puts a patient at a much higher risk of having a breakthrough infection, which can literally cause death because there’s a diminished immune system to fight off the infection,” he concluded.
Disclosure: The research in this study was funded by the National Institutes of Health and the Burroughs Wellcome Fund. For full disclosures of the study authors, visit nature.com.
