Fungal infections during allogeneic hematopoietic cell transplantation (allo-HCT) represent a significant and often perplexing threat to patient health, leading to increased morbidity and mortality. Recent research has shed light on the role of heteroresistance in these infections, particularly focusing on Candida parapsilosis(C. parapsilosis), a common and high-priority pathogen identified by the World Health Organization.
In this groundbreaking study, researchers demonstrated that heteroresistance plays a crucial role in causing breakthrough bloodstream infections in high-risk allo-HCT recipients treated with micafungin prophylaxis. Heteroresistance is defined as the presence of a small and unstable subpopulation of resistant cells (approximately one in 10,000) that can evade treatment, even though most of the population is susceptible to the drug. The team investigated numerous cases where transplant patients developed life-threatening bloodstream infections despite receiving micafungin, a drug often referred to as the penicillin of antifungals. By analyzing 219 clinical isolates of C. parapsilosis from North America, Europe, and Asia, they revealed widespread micafungin heteroresistance in these fungal strains. These findings provided critical insight into the unexplained failures of antifungal prophylaxis in allo-HCT recipients.
Standard antimicrobial susceptibility tests, such as broth microdilution or gradient diffusion assays, are commonly used to guide drug selection for invasive infections. However, these tests fail to detect micafungin heteroresistance in C. parapsilosis, leaving a gap in effective diagnosis and treatment planning. To address this challenge, the researchers, constructed a predictive machine-learning framework. This framework is capable of classifying isolates as heteroresistant or susceptible using a maximum of ten genomic features. This innovative approach offers a rapid and accurate method for detecting heteroresistance, which has the potential to significantly improve clinical decision-making and patient care.
The researchers played a crucial role in developing the machine learning model. Their work demonstrated that by focusing on a small set of genomic features, the algorithm could effectively predict heteroresistance without the need for whole-genome sequencing. There are thousands of mutations, and the algorithm was required to choose at most 10. One of the advantages of machine learning is that there is no need to sequence the whole genome, just find a few spots that are informative enough that they can predict.
Heteroresistance refers to the varying susceptibility to an antimicrobial drug within a microorganism population, where some clones are resistant while others are susceptible. In fungal infections, this means that subpopulations exhibit different levels of resistance to antifungal agents, leading to treatment failures, persistent infections, and increased morbidity and mortality. Detecting heteroresistance is challenging with standard laboratory tests, which often miss these resistant subpopulations, necessitating advanced techniques like population analysis profiling and single-cell analysis. Genetic mechanisms such as mutations and gene amplification can reduce drug efficacy. Advances in next-generation sequencing have shown that fungal resistance can emerge through single mutations, polygenic resistance, transcriptome changes, and aneuploidy. When exposed to antifungals, fungi may develop heteroresistance, where transient aneuploidy confers resistance.
Currently, there is no test for heteroresistance, but researchers should aim to analyze a fecal sample from a patient before a transplant to profile their gut microbes or fungi. If the patient has a micafungin heteroresistant C. parapsilosis, clinicians would be able to choose a different antifungal for prophylaxis or eliminate the fungus from the gut before the transplant. The presence of these fungi significantly increases the risk of a breakthrough infection, which can be fatal due to a weakened immune system’s inability to fight off the infection.
Zhai and colleagues believe this innovation holds promise for developing a simple test to identify heteroresistant fungi in clinical settings. However, they caution that years of research are needed to fully understand the underlying molecular mechanisms. This study marks a significant step forward, paving the way for more effective diagnostics and treatments for vulnerable patients undergoing bone marrow transplants. The research underscores the critical need for advanced diagnostic tools and targeted treatment strategies to combat this persistent and deadly issue.
Reference
- Zhai B, Liao C, Jaggavarapu S, Tang Y, Rolling T, Ning Y, et al. Antifungal heteroresistance causes prophylaxis failure and facilitates breakthrough Candida parapsilosis infections. Nat Med. 2024 Aug 2;1–10.
- Ferreira GF, Santos DA. Heteroresistance and fungi. Mycoses. 2017;60(9):562–8.
- Boyce KJ. The Microevolution of Antifungal Drug Resistance in Pathogenic Fungi. Microorganisms. 2023 Nov;11(11):2757.