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Table_3_Comparison of Two Highly Discriminatory Typing Methods to Analyze Aspergillus fumigatus Azole Resistance.DOCX

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NIAID Data Ecosystem2026-03-10 收录
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https://figshare.com/articles/dataset/Table_3_Comparison_of_Two_Highly_Discriminatory_Typing_Methods_to_Analyze_Aspergillus_fumigatus_Azole_Resistance_DOCX/6844244
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Aspergillus fumigatus molecular typing has become increasingly more important for detecting outbreaks as well as for local and global epidemiological investigations and surveillance. Over the years, many different molecular methods have been described for genotyping this species. Some outstanding approaches are based on microsatellite markers (STRAf assay, which is the current gold standard), or based on sequencing data (TRESP typing improved in this work with a new marker and was renamed TRESPERG). Both methodologies were used to type a collection of 212 A. fumigatus isolates that included 70 azole resistant strains with diverse resistance mechanisms from different geographic locations. Our results showed that both methods are totally reliable for epidemiological investigations showing similar stratification of the A. fumigatus population. STRAf assay offered higher discriminatory power (D = 0.9993) than the TRESPERG typing method (D = 0.9972), but the latter does not require specific equipment or skilled personnel, allowing for a prompt integration into any clinical microbiology laboratory. Among azole resistant isolates, two groups were differentiated considering their resistance mechanisms: cyp51A single point mutations (G54, M220, or G448), and promoter tandem repeat integrations with or without cyp51A modifications (TR34/L98H, TR46/Y121F/A289T, or TR53). The genotypic differences were assessed to explore the population structure as well as the genetic relationship between strains and their azole resistance profile. Genetic cluster analyses suggested that our A. fumigatus population was formed by 6–7 clusters, depending on the methodology. Also, the azole susceptible and resistance population showed different structure and organization. The combination of both methodologies resolved the population structure in a similar way to what has been described in whole-genome sequencing works.
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