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Review
. 2024 Sep 5;20(9):e1012478.
doi: 10.1371/journal.ppat.1012478. eCollection 2024 Sep.

New approaches to tackle a rising problem: Large-scale methods to study antifungal resistance

Philippe C Després  1   2   3 , Rebecca S Shapiro  3 , Christina A Cuomo  1   2
Affiliations
Review

New approaches to tackle a rising problem: Large-scale methods to study antifungal resistance

Philippe C Després et al. PLoS Pathog. .
No abstract available

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Increased sampling improves resistance target discovery and reveals evolutionary patterns.
(a) Mock scenario showcasing the impact of sampling depth in genomics or experimental evolution studies. Here, mutations in 4 different genes can lead to resistance but occur at different rates for each target: ptarget 1 = 8/15, ptarget 2 = 4/15, ptarget 3 = 2/15, and ptarget 4 = 1/15. (b) The chance of detecting each target as a function of sampling as modeled by a multinomial distribution. While both high-frequency targets have approximately 100% chance of being detected in under 10 samples, having a more than 90% chance of identifying all genes requires>40 samples. (c) If trying to prioritize the most common driver of resistance for downstream studies or interventions, low sample size can lead to misleading conclusions. Estimating the relative contribution of each target is difficult if sampling is limited, as shown by the large overlap between the 5th to 95th percentiles of hit rates measured below 25 samples. (d) Additional reference genomes for SNP mapping increase the number of associations with resistance and virulence phenotypes. Dutta and colleagues [12] performed a GWAS for 49 life history traits on a panel of 145 Zymoseptoria tritici strains. Including additional reference genomes for SNP mapping increased the number of significant orthogroup-trait associations by up to a third. (e) Experimental evolution along an antifungal gradient uncovers dose-dependent effects on structural variation. Todd and colleagues [13] evolved Candida albicans strains at different fluconazole concentrations and found that while whole chromosome aneuploidies were more common at high concentrations in the SC5314 genetic background, this pattern was reversed for segmental aneuploidies, hinting at differences in fitness trade-offs for these 2 types of structural variants.
Fig 2
Fig 2. MAVEs systematically characterize the phenotypes of protein variants.
The coding sequence of a gene of interest (GOI) is mutagenized to generate a library of mutant alleles, usually resulting in 1 amino acid substitution per protein. These alleles are then batch-transformed into a recipient strain to generate a large pool of variants that can be competed against one another under selective pressure like antifungal exposure. The abundance of each variant can be tracked by deep sequencing the locus of interest or a DNA barcode region that serves as an identifier for variants. By following the relative abundance in sequencing data of each variant at different pooled competition time points and comparing it to those of wild-type alleles, the fitness of each variant can be inferred. By modulating experimental conditions, the effect of variants on both resistance and fitness can be measured and the trade-off between the 2 can be characterized. In the case of the azole target ERG11 [37], most resistant variants were found to have little impact on fitness, resulting in next to no compromise between the two. Conversely, mutations in the 5-FC target FCY1 [36] granting even a small amount of resistance resulted in an almost full loss of function, signaling a strong resistance-fitness trade-off.
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References

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