Fisher, M. C. et al. Threats posed by the fungal kingdom to humans, wildlife, and agriculture. MBio 11, e00449–20 (2020).
Google Scholar
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).
Google Scholar
Nash, A. et al. MARDy: Mycology Antifungal Resistance Database. Bioinformatics 34, 3233–3234 (2018).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Yang, F. et al. The fitness costs and benefits of trisomy of each Candida albicans chromosome. Genetics 218, iyab056 (2021).
Google Scholar
Kanafani, Z. A. & Perfect, J. R. Antimicrobial resistance: resistance to antifungal agents: mechanisms and clinical impact. Clin. Infect. Dis. 46, 120–128 (2008).
Google Scholar
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).
Google Scholar
Longley, D. B., Harkin, D. P. & Johnston, P. G. 5-fluorouracil: mechanisms of action and clinical strategies. Nat. Rev. Cancer 3, 330–338 (2003).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Dionne, U. et al. Protein context shapes the specificity of SH3 domain-mediated interactions in vivo. Nat. Commun. 12, 1597 (2021).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Ivankov, D. N., Finkelstein, A. V. & Kondrashov, F. A. A structural perspective of compensatory evolution. Curr. Opin. Struct. Biol. 26, 104–112 (2014).
Google Scholar
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).
Google Scholar
Tarassov, K. An in vivo map of the yeast protein interactome. Science 320, 1465–1470 (2008).
Google Scholar
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).
Google Scholar
Chang, A. et al. BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res. 49, D498–D508 (2021).
Google Scholar
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Hoenigl, M. et al. The antifungal pipeline: fosmanogepix, ibrexafungerp, olorofim, opelconazole, and rezafungin. Drugs https://doi.org/10.1007/s40265-021-01611-0 (2021).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Chang, Y. C. et al. Moderate levels of 5-fluorocytosine cause the emergence of high frequency resistance in cryptococci. Nat. Commun. 12, 3418 (2021).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Gietz, R. D. & Schiestl, R. H. High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method. Nat. Protoc. 2, 31–34 (2007).
Google Scholar
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).
Google Scholar
Goldstein, A. L. & McCusker, J. H. Three new dominant drug resistance cassettes for gene disruption in Saccharomyces cerevisiae. Yeast 15, 1541–1553 (1999).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Yachie, N. et al. Pooled-matrix protein interaction screens using barcode fusion genetics. Mol. Syst. Biol. 12, 863 (2016).
Google Scholar
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).
Google Scholar
Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
Google Scholar
Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).
Google Scholar
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).
Google Scholar
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Schymkowitz, J. et al. The FoldX web server: an online force field. Nucleic Acids Res. 33, W382–W388 (2005).
Google Scholar
Marchant, A. et al. The role of structural pleiotropy and regulatory evolution in the retention of heteromers of paralogs. eLife 8, e46754 (2019).
Google Scholar
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).
Google Scholar
Howe, K. L. et al. Ensembl 2021. Nucleic Acids Res. 49, D884–D891 (2021).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 5, 113 (2004).
Google Scholar
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).
Google Scholar
Lõoke, M., Kristjuhan, K. & Kristjuhan, A. Extraction of genomic DNA from yeasts for PCR-based applications. Biotechniques 50, 325–328 (2011).
Google Scholar
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).
Google Scholar
Diss, G. & Lehner, B. The genetic landscape of a physical interaction. eLife 7, e32472 (2018).
Google Scholar
Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).
Google Scholar
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