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Asymmetrical dose responses shape the evolutionary trade-off between antifungal resistance and nutrient use

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Abstract

Antimicrobial resistance is an emerging threat for public health. The success of resistance mutations depends on the trade-off between the benefits and costs they incur. This trade-off is largely unknown and uncharacterized for antifungals. Here, we systematically measure the effect of all amino acid substitutions in the yeast cytosine deaminase Fcy1, the target of the antifungal 5-fluorocytosine (5-FC, flucytosine). We identify over 900 missense mutations granting resistance to 5-FC, a large fraction of which appear to act through destabilization of the protein. The relationship between 5-FC resistance and growth sustained by cytosine deamination is characterized by a sharp trade-off, such that small gains in resistance universally lead to large losses in canonical enzyme function. We show that this steep relationship can be explained by differences in the dose–response functions of 5-FC and cytosine. Finally, we observe the same trade-off shape for the orthologue of FCY1 in Cryptoccocus neoformans, a human pathogen. Our results provide a powerful resource and platform for interpreting drug target variants in fungal pathogens as well as unprecedented insights into resistance–function trade-offs.

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Fig. 1: Fcy1 as a model for resistance–function trade-offs.
Fig. 2: The functional landscape of Fcy1 identifies all resistance mutations.
Fig. 3: Fcy1 amino acid substitutions can lead to 5-FC resistance through multiple mechanisms.
Fig. 4: Fcy1 mutants are constrained to a small region of phenotypic space.
Fig. 5: Dose–response parameters predict the properties of Fcy1 mutants.
Fig. 6: The resistance–function trade-off is conserved in the yeast FCY1 orthologue of the pathogen C. neoformans.

Data availability

Raw sequencing files for the DMS libraries and the competition screen have been deposited on the NCBI SRA (accession number PRJNA782569). All raw images (source data for Supplementary Fig. 2 and Fig. 6b and Extended Data Fig. 9) are provided as Supplementary Data 13 and 15. Supplementary Data 5–10 provide the source data for the DMS experiments and NGS analysis, the validation studies, the dose–response assays and the experiments in the C. neoformans orthologue.

Code availability

Scripts used for data analysis and figure generation are available at https://github.com/Landrylab/Despres_et_al_2021.

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Acknowledgements

We thank members of the Landry lab for helpful discussions, in particular D. Evans-Yamamoto, M. Hénault, F. Mattenberger and R. Durand. This work was supported by the Canadian Institutes of Health Research Foundation grant number 387697 to C.R.L. and a Vanier graduate scholarship to P.C.D, as well as by the National Science and Engineering Research Council through the EvoFunPath CREATE grant (number 555337-2021) and by FRQNT through team grant (number 2022-PR-298169) and a PBEEE scholarship to A.F.C. C.R.L. holds the Canada Research Chair in in Cellular Systems and Synthetic Biology. Molecular graphics and analyses were performed with UCSF ChimeraX, developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from National Institutes of Health R01-GM129325 and the Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases.

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P.C.D., A.K.D. and C.R.L. designed the research. P.C.D., E.M.M.A., C.G-T. and A.K.D. performed the experiments. P.C.D., A.F.C., R.S. and C.R.L performed the data analysis. P.C.D. and C.R.L. wrote the paper with input from all authors.

Corresponding authors

Correspondence to Philippe C. Després or Christian R. Landry.

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Extended data

Extended Data Fig. 1 Replicate amino acid log2 fold-changes in the DMS experiments.

Correlation between R1 and R2 was measured using Spearman’s rank correlation. a) Pool 1, 5-FC (n = 1372). b) Pool 2, 5-FC (n = 1298). c) Pool 3, 5-FC (n = 1374). d) Pool 1, cytosine (n = 1372). e) Pool 2, cytosine (n = 1298). f) Pool 3, cytosine (n = 1374). g) Pool 2, 5-FC + Cytosine (n = 1298).

Extended Data Fig. 2 Using pool overlapping fragments to harmonise the log2 fold-changes of the FCY1 mutant pools.

For each panel, the linear least-squares regression parameters are shown, with Spearman’s rank correlation shown below. a) Pool 1 to Pool 2, cytosine (n = 397). b) Pool 3 to Pool 2, cytosine (n = 375). c) Pool 1 to Pool 2, 5-FC (n = 397). d) Pool 3 to Pool 2, 5-FC (n = 375).

Extended Data Fig. 3 Scaling log2 fold-changes to pool 2 and scaling scores to synonymous and nonsense mutations.

For panel A-F, the linear least-squares regression parameters are shown, with Spearman’s rank correlation shown below for the comparison between raw pool scores and pool 2 adjusted scores. a) Raw pool 1 vs adjusted pool 1, cytosine (n = 1372) b) Raw pool 2 vs adjusted pool 2, cytosine (n = 1298). c) Raw pool 3 vs adjusted pool 3, cytosine (n = 1374). d) Raw pool 1 vs adjusted pool 1, 5-FC (n = 1372) e) Raw pool 2 vs adjusted pool 2, 5-FC (n = 1298). f) Raw pool 3 vs adjusted pool 3, 5-FC (n = 1374). g) Adjusted log2 fold-change for synonymous (black) and nonsense (red) mutants in cytosine (n = 148 synonymous, n = 156 nonsense mutants) along the protein positions. For positions where Met and Trp are the wild-type amino acids, there are no synonymous codons. This occurs 8 times in the FCY1 coding sequence (not including the start Met). h) Adjusted log2 fold-change for synonymous (black) and nonsense (red) mutants in 5-FC (n = 148 synonymous, n = 156 nonsense mutants).

Extended Data Fig. 4 DMSscore distributions by mutant type.

Silent mutations are shown in green, nonsense in magenta, and missense mutants in grey. a) 5-FC (n = 148 silent, 151 nonsense, 2968 missense). b) Cytosine (n = 148 silent, 151 nonsense, 2968 missense) c) 5-FC + cytosine (n = 59 silent, 62 nonsense, 1177 missense). d) DMSscore in 5-FC + cytosine as a function of scores in cytosine only with spearman’s rank correlation shown (n = 1298 mutants).

Extended Data Fig. 5 FCY1 evolutionary rate and orthologous residue diversity.

a) frequency of the S. cerevisiae amino acid at each position in the orthologous sequences. b) Normalized evolutionary rate for all Fcy1 residues (Rate4site24) from 215 orthologues. The blue line represents the rolling average over a 6 amino acid window. This statistic represents the rate at which amino acids change along a phylogeny: higher values represent more variable positions, while lower values represent more conserved positions. c) Multiple sequence alignment coverage of S. cerevisiae Fcy1 positions in the set of orthologues. The maximum value is 215, representing perfectly conserved amino acids positions across all sequences.

Extended Data Fig. 6 DHFR-PCA data supports protein structure stability predictions for validation mutants.

FCY1 variants were tagged with a DHFR-PCA25 fragment to measure protein complex formation with a wild-type copy of FCY1. In this approach, Fcy1 is fused to DHFR fragments that complement upon dimerization, allowing growth in media containing methotrexate (MTX). Growth reflects the amount of complex formed, therefore providing a quantitative measure of the stability of the Fcy1 subunits and complex. The 54 mutants are colored based on their DMS cluster (see Extended Data Fig. 7). All panels show Spearman’s rank correlation. a) Growth rates in DMSO of the validation mutants for the two replicates. DMSO is the MTX solvent and is the control condition for the DHFR-PCA assay. b) Growth rates in MTX of the validation mutants for the two replicates. c) Growth rate in MTX as a function of their growth rate in DMSO. As expected, there is no strong correlation between the two. d) Growth rate in MTX as a function of FoldX58 predicted change in Fcy1 structure stability measured as ΔΔG. Positive ΔΔG represents destabilization. e) Growth rate in MTX as a function of the growth rate in cytosine media of the haploid strain. f) Growth rate in MTX and in 5-FC media of the haploid strain.

Extended Data Fig. 7 DMS assay validations in 5-FC and cytosine media.

a) Location on the cytosine/5-FC landscape of the Fcy1 variants (shown as grey dots) selected for validations superimposed on the density plot presented in Fig. 2d. The circles used to define the three clusters are also shown: green for silent-like mutants, magenta for nonsense-like mutants, and blue for front minimum mutants. Mutants falling outside these clusters were classified as ‘other’ and encompass most outliers from the DMS screen. Variants with both high 5-FC and cytosine DMSscore are shown as squares: these outliers potentially escape the resistance-function trade-off. b) Spearman’s correlation between growth curve replicates in 5-FC media, n = 73 variants. c) Spearman’s correlation between growth curve replicates in cytosine media, n = 72 variants. Data collected from the cytosine media from the outlier (T86M) was excluded from downstream analysis. d) Linear least-squares regression fit and Spearman’s correlation between 5-FC growth rates measured in the 1st and 2nd rounds of validations for 17 mutants present in both growth curve assays. e) Linear least-squares regression fit and Spearman’s correlation between cytosine growth rates measured in the 1st and 2nd rounds of validations for 17 mutants present in both assays. f) Growth rate values of the 2nd round of validations scaled to the growth rate of the 1st round for the 17 mutants present in both assays. g) Spearman’s correlation between validation growth curves in 5-FC media and DMS 5-FC score, n = 79 variants. h) Spearman’s correlation between validation growth curves in cytosine media and DMS cytosine score, n = 79 variants. i) Spearman’s correlation between validation growth curves in cytosine media and DMS 5-FC + cytosine score, n = 38 variants.

Extended Data Fig. 8 Phenotypes of the scFcy1 and cnFcy1 mutants.

a) Growth rate for the 29 (27 of which were successfully constructed) scFCY1 variants selected in the DMS assay validation experiments. b) Representative examples of the phenotypes observed in the spot assays (Synthetic media + 194 μM 5-FC, 10-fold dilutions starting at 1 OD/ml), where S: sensitive, r: low growth and R: full resistance. c) Phenotypes of scFCY1_opt (S. cerevisiae codon optimized FCY1 at the FCY1 locus), cnFCY1_opt (C. neoformans codon optimized FCY1 at the S. cerevisiae FCY1 locus) compared to the parental strain (BY4742) and the deletion mutant (Δfcy1). d) Comparison of growth rate between orthologous variants in 5-FC media. Spearman’s rank correlation is shown (n = 22 pairs). Variants are colored by the position along the trade-off of the scFCY1 variant. e) Comparison of 5-FC media growth rate between orthologous variants. Spearman’s rank correlation is shown (n = 22 pairs). f) Changes in growth rate in SC + 12 μM 5-FC and SC-Ura + 84 μM cytosine between scFcy1 and cnFcy1 variants. Spearman’s rank correlation is shown (n = 22 pairs).

Extended Data Fig. 9 Spot dilution assay phenotypes are most often conserved between orthologous mutants of scFcy1 and cnFcy1.

The same dilutions of control strains BY4742 (WT FCY1), scFCY1_opt (S. cerevisiae codon optimized FCY1 at the FCY1 locus), cnFCY1_opt (C. neoformans codon optimized FCY1 at the S. cerevisiae FCY1 locus) and Δfcy1 were spotted on each plate. For each mutant pair, the scFcy1 strain is in white and the cnFcy1 is highlighted in grey. The phenotype score (as defined earlier) for each strain is shown on the right. The raw images used to generate the figure are available as Supplementary Data 5.

Extended Data Table 1 DMSscores predicts resistance phenotype in clinical isolates. Variant 5-FC DMSscores recapitulates known resistance associated mutations in fungal pathogens

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Després, P.C., Cisneros, A.F., Alexander, E.M.M. et al. Asymmetrical dose responses shape the evolutionary trade-off between antifungal resistance and nutrient use. Nat Ecol Evol 6, 1501–1515 (2022). https://doi.org/10.1038/s41559-022-01846-4

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