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.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
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.
Scripts used for data analysis and figure generation are available at https://github.com/Landrylab/Despres_et_al_2021.
Fisher, M. C. et al. Threats posed by the fungal kingdom to humans, wildlife, and agriculture. MBio 11, e00449–20 (2020).
Fisher, M. C., Hawkins, N. J., Sanglard, D. & Gurr, S. J. Worldwide emergence of resistance to antifungal drugs challenges human health and food security. Science 360, 739–742 (2018).
Nash, A. et al. MARDy: Mycology Antifungal Resistance Database. Bioinformatics 34, 3233–3234 (2018).
Ksiezopolska, E. et al. Narrow mutational signatures drive acquisition of multidrug resistance in the fungal pathogen Candida glabrata. Curr. Biol. 4, 5314–5326.e10 (2021).
Bryce Taylor, M. et al. yEvo: Experimental evolution in high school classrooms selects for novel mutations and epistatic interactions that impact clotrimazole resistance in S. cerevisiae. Preprint at bioRxiv https://doi.org/10.1101/2021.05.02.442375 (2021).
Andersson, D. I. & Hughes, D. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat. Rev. Microbiol. 8, 260–271 (2010).
Gerstein, A. C., Lo, D. S. & Otto, S. P. Parallel genetic changes and nonparallel gene-environment interactions characterize the evolution of drug resistance in yeast. Genetics 192, 241–252 (2012).
Yang, F. et al. The fitness costs and benefits of trisomy of each Candida albicans chromosome. Genetics 218, iyab056 (2021).
Kanafani, Z. A. & Perfect, J. R. Antimicrobial resistance: resistance to antifungal agents: mechanisms and clinical impact. Clin. Infect. Dis. 46, 120–128 (2008).
Iyer, K. R., Revie, N. M., Fu, C., Robbins, N. & Cowen, L. E. Treatment strategies for cryptococcal infection: challenges, advances and future outlook. Nat. Rev. Microbiol. 19, 454–466 (2021).
Longley, D. B., Harkin, D. P. & Johnston, P. G. 5-fluorouracil: mechanisms of action and clinical strategies. Nat. Rev. Cancer 3, 330–338 (2003).
Erbs, P., Exinger, F. & Jund, R. Characterization of the Saccharomyces cerevisiae FCY1 gene encoding cytosine deaminase and its homologue FCA1 of Candida albicans. Curr. Genet. 31, 1–6 (1997).
Wrenbeck, E. E., Azouz, L. R. & Whitehead, T. A. Single-mutation fitness landscapes for an enzyme on multiple substrates reveal specificity is globally encoded. Nat. Commun. 8, 15695 (2017).
Chen, J. Z., Fowler, D. M. & Tokuriki, N. Comprehensive exploration of the translocation, stability and substrate recognition requirements in VIM-2 lactamase. eLife 9, e56707 (2020).
Li, A., Acevedo-Rocha, C. G. & Reetz, M. T. Boosting the efficiency of site-saturation mutagenesis for a difficult-to-randomize gene by a two-step PCR strategy. Appl. Microbiol. Biotechnol. 102, 6095–6103 (2018).
Biot-Pelletier, D. & Martin, V. J. J. Seamless site-directed mutagenesis of the Saccharomyces cerevisiae genome using CRISPR-Cas9. J. Biol. Eng. 10, 6 (2016).
Dionne, U. et al. Protein context shapes the specificity of SH3 domain-mediated interactions in vivo. Nat. Commun. 12, 1597 (2021).
Eddy, A. A. Expulsion of uracil and thymine from the yeast Saccharomyces cerevisiae: contrasting responses to changes in the proton electrochemical gradient. Microbiology 143, 219–229 (1997).
Kurtz, J. E., Exinger, F., Erbs, P. & Jund, R. New insights into the pyrimidine salvage pathway of Saccharomyces cerevisiae: requirement of six genes for cytidine metabolism. Curr. Genet. 36, 130–136 (1999).
Fujimura, H. Growth inhibition of Saccharomyces cerevisiae by the immunosuppressant leflunomide is due to the inhibition of uracil uptake via Fur4p. Mol. Gen. Genet. 260, 102–107 (1998).
Després, P. C., Dubé, A. K., Nielly-Thibault, L., Yachie, N. & Landry, C. R. Double selection enhances the efficiency of Target-AID and Cas9-based genome editing in yeast. G3 8, 3163–3171 (2018).
Wang, J. et al. Role of glutamate 64 in the activation of the prodrug 5-fluorocytosine by yeast cytosine deaminase. Biochemistry 51, 475–486 (2012).
Ivankov, D. N., Finkelstein, A. V. & Kondrashov, F. A. A structural perspective of compensatory evolution. Curr. Opin. Struct. Biol. 26, 104–112 (2014).
Mayrose, I., Graur, D., Ben-Tal, N. & Pupko, T. Comparison of site-specific rate-inference methods for protein sequences: empirical Bayesian methods are superior. Mol. Biol. Evol. 21, 1781–1791 (2004).
Tarassov, K. An in vivo map of the yeast protein interactome. Science 320, 1465–1470 (2008).
Freschi, L., Torres-Quiroz, F., Dubé, A. K. & Landry, C. R. qPCA: a scalable assay to measure the perturbation of protein–protein interactions in living cells. Mol. Biosyst. 9, 36–43 (2013).
Chang, A. et al. BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res. 49, D498–D508 (2021).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Mirdita, M. et al. ColabFold – Making protein folding accessible to all. Preprint at bioRxiv https://doi.org/10.1101/2021.08.15.456425 (2022).
Pokusaeva, V. O. et al. An experimental assay of the interactions of amino acids from orthologous sequences shaping a complex fitness landscape. PLoS Genet. 15, e1008079 (2019).
Oliver, J. D. et al. F901318 represents a novel class of antifungal drug that inhibits dihydroorotate dehydrogenase. Proc. Natl Acad. Sci. USA 113, 12809–12814 (2016).
Hoenigl, M. et al. The antifungal pipeline: fosmanogepix, ibrexafungerp, olorofim, opelconazole, and rezafungin. Drugs https://doi.org/10.1007/s40265-021-01611-0 (2021).
Verweij, P. E., Te Dorsthorst, D. T. A., Janssen, W. H. P., Meis, J. F. G. M. & Mouton, J. W. In vitro activities at pH 5.0 and pH 7.0 and in vivo efficacy of flucytosine against Aspergillus fumigatus. Antimicrob. Agents Chemother. 52, 4483–4485 (2008).
Gsaller, F. et al. Mechanistic basis of pH-dependent 5-flucytosine resistance in Aspergillus fumigatus. Antimicrob. Agents Chemother. https://doi.org/10.1128/AAC.02593-17 (2018).
Garland, T. Jr. Trade-offs. Curr. Biol. 24, R60–R61 (2014).
Chang, Y. C. et al. Moderate levels of 5-fluorocytosine cause the emergence of high frequency resistance in cryptococci. Nat. Commun. 12, 3418 (2021).
Billmyre, R. B., Applen Clancey, S., Li, L. X., Doering, T. L. & Heitman, J. 5-fluorocytosine resistance is associated with hypermutation and alterations in capsule biosynthesis in Cryptococcus. Nat. Commun. 11, 127 (2020).
Brachmann, C. B. et al. Designer deletion strains derived from Saccharomyces cerevisiae S288C: a useful set of strains and plasmids for PCR-mediated gene disruption and other applications. Yeast 14, 115–132 (1998).
Gietz, R. D. & Schiestl, R. H. High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method. Nat. Protoc. 2, 31–34 (2007).
Janke, C. et al. A versatile toolbox for PCR-based tagging of yeast genes: new fluorescent proteins, more markers and promoter substitution cassettes. Yeast 21, 947–962 (2004).
Goldstein, A. L. & McCusker, J. H. Three new dominant drug resistance cassettes for gene disruption in Saccharomyces cerevisiae. Yeast 15, 1541–1553 (1999).
DeLuna, A., Springer, M., Kirschner, M. W. & Kishony, R. Need-based up-regulation of protein levels in response to deletion of their duplicate genes. PLoS Biol. 8, e1000347 (2010).
Casadaban, M. J. & Cohen, S. N. Analysis of gene control signals by DNA fusion and cloning in Escherichia coli. J. Mol. Biol. 138, 179–207 (1980).
Yachie, N. et al. Pooled-matrix protein interaction screens using barcode fusion genetics. Mol. Syst. Biol. 12, 863 (2016).
Andrews, S. FastQC: A quality control analysis tool for high throughput sequencing data (Babraham Bioinformatics, 2016); https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).
Reback, J. et al. pandas-dev/pandas: Pandas 1.3.4. Zenodo https://doi.org/10.5281/zenodo.5574486 (2021).
Waskom, M. seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G. & Neufeld, J. D. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinform. https://doi.org/10.1186/1471-2105-13-31 (2012).
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
Rice, P., Longden, L. & Bleasby, A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. https://doi.org/10.1016/S0168-9525(00)02024-2 (2000).
Ryan, O. W., Poddar, S. & Cate, J. H. D. Crispr–cas9 genome engineering in Saccharomyces cerevisiae cells. Cold Spring Harb. Protoc. https://doi.org/10.1101/pdb.prot086827 (2016).
Amberg, D. C., Burke, D. J. & Strathern, J. N. Methods in Yeast Genetics: A Cold Spring Harbor Laboratory Course Manual (CSHL Press, 2005).
Ireton, G. C., Black, M. E. & Stoddard, B. L. The 1.14 A crystal structure of yeast cytosine deaminase: evolution of nucleotide salvage enzymes and implications for genetic chemotherapy. Structure 11, 961–972 (2003).
Schymkowitz, J. et al. The FoldX web server: an online force field. Nucleic Acids Res. 33, W382–W388 (2005).
Marchant, A. et al. The role of structural pleiotropy and regulatory evolution in the retention of heteromers of paralogs. eLife 8, e46754 (2019).
Usmanova, D. R. et al. Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. Bioinformatics 34, 3653–3658 (2018).
Howe, K. L. et al. Ensembl 2021. Nucleic Acids Res. 49, D884–D891 (2021).
Chorostecki, U., Molina, M., Pryszcz, L. P. & Gabaldón, T. MetaPhOrs 2.0: integrative, phylogeny-based inference of orthology and paralogy across the tree of life. Nucleic Acids Res. 48, W553–W557 (2020).
Byrne, K. P. & Wolfe, K. H. The Yeast Gene Order Browser: combining curated homology and syntenic context reveals gene fate in polyploid species. Genome Res. 15, 1456–1461 (2005).
Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 5, 113 (2004).
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).
Lõoke, M., Kristjuhan, K. & Kristjuhan, A. Extraction of genomic DNA from yeasts for PCR-based applications. Biotechniques 50, 325–328 (2011).
Schlecht, U., Miranda, M., Suresh, S., Davis, R. W. & St Onge, R. P. Multiplex assay for condition-dependent changes in protein-protein interactions. Proc. Natl Acad. Sci. USA 109, 9213–9218 (2012).
Diss, G. & Lehner, B. The genetic landscape of a physical interaction. eLife 7, e32472 (2018).
Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).
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.
The authors declare no competing interests.
Peer review information
Nature Ecology & Evolution thanks Nobuhiko Tokuriki and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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).
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).
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.
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.
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.
About this article
Cite this article
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
This article is cited by
Nature Reviews Microbiology (2022)