2026-06-14T02:29:49Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/163022024-11-28T12:36:36Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19609col_20.500.12105_25179
Repisalud
author
Alastruey-Izquierdo, Ana
author
Martin-Galiano, Antonio Javier
funder
Instituto de Salud Carlos III
2023-07-20T14:02:54Z
2023-07-20T14:02:54Z
2023
Front Microbiol. 2023 Apr 20;14:1134755.
10.3389/fmicb.2023.1134755
1664-302X
Frontiers in microbiology
37152754
http://hdl.handle.net/20.500.12105/16302
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.
eng
Amphotericin B
Antimicrobial target
Aspergillus
Azoles
Candida
Cryptococcus
Echinocandins
Polyenes
The challenges of the genome-based identification of antifungal resistance in the clinical routine
research article
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ChallengesGenome-basedIdentification_2023.pdf
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ChallengesGenome-basedIdentification_2023.pdf.txt