The challenges of the genome-based identification of antifungal resistance in the clinical routine

Abstract

The increasing number of chronic and life-threatening infections caused by antimicrobial resistant fungal isolates is of critical concern. Low DNA sequencing cost may facilitate the identification of the genomic profile leading to resistance, the resistome, to rationally optimize the design of antifungal therapies. However, compared to bacteria, initiatives for resistome detection in eukaryotic pathogens are underdeveloped. Firstly, reported mutations in antifungal targets leading to reduced susceptibility must be extensively collected from the literature to generate comprehensive databases. This information should be complemented with specific laboratory screenings to detect the highest number possible of relevant genetic changes in primary targets and associations between resistance and other genomic markers. Strikingly, some drug resistant strains experience high-level genetic changes such as ploidy variation as much as duplications and reorganizations of specific chromosomes. Such variations involve allelic dominance, gene dosage increments and target expression regime effects that should be explicitly parameterized in antifungal resistome prediction algorithms. Clinical data indicate that predictors need to consider the precise pathogen species and drug levels of detail, instead of just genus and drug class. The concomitant needs for mutation accuracy and assembly quality assurance suggest hybrid sequencing approaches involving third-generation methods will be utilized. Moreover, fatal fast infections, like fungemia and meningitis, will further require both sequencing and analysis facilities are available in-house. Altogether, the complex nature of antifungal resistance demands extensive sequencing, data acquisition and processing, bioinformatic analysis pipelines, and standard protocols to be accomplished prior to genome-based protocols are applied in the clinical setting.

ResFungi: A Novel Protein Database of Antifungal Drug Resistance Genes Using a Hidden Markov Model Profile

“…As more genes are described as conferring antifungal resistance, these data will be added to ResFungi. Moreover, genome-based predictors for antifungal resistance are still unavailable, while such predictors are widely used for microbial resistance. , Therefore, ResFungi is not only a pioneer database for future studies and databases but allows the description of candidate antifungal resistance genes in species without prior resistance studies.…”

“…Among the critical group in the fungal priority pathogen list published by the World Health Organization are the yeasts: Candida albicans, Candida auris, and Cryptococcus neoformans. , Candida species have been targeted as a serious pathogen due to their increasing worldwide spread and multidrug resistance. Candida albicans and Candida auris strains resistant to fluconazole have been widely isolated, and voriconazole resistance has also been described for C.…”

“…Interestingly, HMM protein profiles can be found for antibiotic resistance genes in bacteria, for instance, Resfams and AMRFinderPlus, allowing studies that describe bacterial resistomes. However, resistome predictors for fungi are not yet available …”

Molecular Diagnostics for Invasive Fungal Diseases: Current and Future Approaches

“…Another key study area of NGS use is in the area of antifungal drug resistance. Whilst targeted assays looking at commonly described resistances genes have been developed (discussed further in Section 4), novel mechanisms of resistance often require further genomic characterisation [170,171]. Furthermore, NGS has been successfully used to track intra-host evolution of pathogens, especially in cases where antifungal resistance has developed in vivo [172].…”

Most azole antifungal resistance mutations in the drug target provide cross-resistance and carry no intrinsic fitness cost

“…genomes. Genomic tools could be useful to detect and monitor resistance mutations in a pathogen infecting a patient in real time throughout treatment, allowing for the adjustment of therapeutic use prior to treatment failure [26][27][28] . Another benefit from such knowledge would be to potentially modify current drugs to overcome or prevent resistance.…”