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Finding the needle in a haystack: Mapping antifungal drug resistance in fungal pathogen by genomic approaches

Finding the needle in a haystack: Mapping antifungal drug resistance in fungal pathogen by genomic approaches

  • Dominique Sanglard
PLOS
x

Fungi strike back when attacked

Fungi are ubiquitous on earth and are essential for the maintenance of the global ecological equilibrium. Despite providing benefits to living organisms, they can also target specific hosts and inflict damage. These fungal pathogens are known to affect, for example, plants and mammals and thus reduce crop production necessary to sustain food supply and cause mortality in humans and animals. Designing defenses against these fungi is essential for the control of food resources and human health. As far as fungal pathogens are concerned, the principal option has been the use of antifungal agents, also called fungicides when they are used in the environment.

Commercial antifungal agents and fungicides belong to several classes and affect several cellular functions (Fig 1) [1]. Fungicides outnumber medical antifungal agents by the number of chemical classes and by the number of licensed drugs [1]. Exposing plant and/or human pathogens to these agents results in a substantial fungal growth deficiency and eventually clearance of the pathogens in the infected sites or infected host. However, the same exposure imposes strong selective pressures for the development of resistance, which is ineluctably acquired over short- and long-term periods. Fungicides that are spread in the environment can also cause the selection of resistance in ubiquitous fungi that are also human pathogens. The transmission of environmentally acquired resistance to human is of concern because it reduces the limited number of possible treatments to a few alternatives.

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Fig 1. Cellular targets of principal antifungals used in medicine and in agriculture.

The cited antifungals and fungicides have the following mode of action: polyenes bind ergosterol; phenylpyrroles and dicarboximides inhibit MAP/histidine-kinases; azoles and morpholines target 14α-lanosterol demethylase and both sterol Δ14-reductase and Δ8-Δ7-isomerase, respectively (both chemical classes are used in medicine and in the environment); allylamines target squalene epoxidase; pyrimidine analogues interfere with nucleic acid biosynthesis whereas phenylamides target RNA polymerase I; benzimidazoles bind to b-tubulin; strobilurins and succinate dehydrogenase inhibitors inhibit both the electron transfer chain of mitochondrial respiration by inhibiting complex III and complex II, respectively; anilinopyrimidines target mitochondrial signaling pathways; candins target the biosynthesis of β-1,3 glucans. MAP, Mitogen-activated protein.

https://doi.org/10.1371/journal.ppat.1007478.g001

It is important to resolve resistance mechanisms because they may highlight not only novel biology but also may reveal novel strategies to counteract and combat resistance. Resistance mechanisms can be grouped in at least three general principles that include (i) decrease of effective intracellular drug concentration, (ii) alterations of the drug target, and (iii) compensatory mechanisms which decrease drug toxicity [2]. I will summarize here approaches that led to the understanding of antifungal resistance in plant and human fungal pathogens with a special focus on genome-wide studies.

Reverse and forward genetics to understand drug resistance

Genome-wide studies performed on the yeast Saccharomyces cerevisiae have been crucial to decipher the mode of action of drugs and associated resistance mechanisms [3]. These studies are based on reverse genetics with the use of mutant collections and/or overexpression collections that can be scaled to high throughput screenings [4]. Genetic screenings can reveal not only the principal target of antifungal agents but also identify off-targets which constitute genetic networks that are affected by drug exposure [5]. Several examples of such genetic screens have been reported in human fungal pathogens. In Candida albicans, homozygous and heterozygous mutant collections (a total of 3,030 mutants) were used to identify gene networks that contribute to resistance to candins. In the same species, overexpression of all transcription factors of a specific family (zinc cluster family) helped to identify novel mediators of azole resistance [6]. In C. glabrata, a collection of deletion mutants (619) was used to identify genes critical for azole and candin stress [7]. In Cryptococcus neoformans, a collection of transcription factor mutants (322) explored gene networks mediating resistance to azoles, polyenes, and 5-fluorocytosine [8].

Forward genetics and the systematic generation of drug-resistant mutants can also be used to discover the mode of action of drugs. The identification of the mutation(s) responsible for the drug resistance phenotype can be addressed by classical genetic analysis of the mutants. This requires, however, several lengthy processes, including, for example, back-crosses with tester strains and/or complementation tests with whole-genome libraries [9]. A classic example in early studies is the discovery of the targets of rapamycin using mutants generated in S. cerevisiae [10].

This classical forward genetic approach can now be greatly facilitated by whole-genome sequence analysis. With the technical advances of DNA sequencing technologies, whole genomes can now be sequenced with high efficiency and at low cost. As early as 2008, the use of a whole-genome approach (by pyrosequencing) addressed the occurrence of spontaneous mutations in S. cerevisiae [11]. This study was followed in 2010 by another genome-scale study (using pyrosequencing) in S. cerevisiae that identified gain of function alleles of peroxiredoxin (TSA1) which conferred resistance to oxidative stress [12]. Elucidation of drug resistance mechanisms has recently progressed by the use of hypermutator strains in S. cerevisiae lacking a functional MSH2 gene [13]. This gene participates in DNA mismatch repair, and when its activity is decreased either by mutations or gene inactivation, it induces an increased rate of spontaneous mutations or drug-induced mutations [14]. The study of Ojini and colleagues [13] exploited this property to generate mutants resistant to several drugs including canavanine, camptothecin, and several anticancer drugs. Systematic genome sequencing of drug-resistant mutants revealed mutations in genes critical for drug resistance, e.g., CAN1 for canavanine and TOP1 for camptothecin [13]. The use of hypermutator S. cerevisiae strains with an MSH2 defect followed by whole-genome sequencing was recently used to identify the gene (ERG6) targeted by the natural antifungal compound tomatidine [15]. These drug resistance mapping studies underline the great potential of whole-genome sequencing. This specific use of genome sequencing is, however, only a small fraction of the vast majority of other genome sequencing studies performed with fungi that were aimed to resolve population genetic structures in S. cerevisiae [16] or even C. albicans [17].

Genome-wide studies to understand drug resistance in fungal pathogens

Drug-resistant fungal pathogens originating from the environment or from the clinic constitute an immense resource for understanding antifungal drug resistance mechanisms. Whole-genome sequence data and downstream analysis give unique opportunities to identify different types of mutations, including single nucleotide polymorphisms (SNPs), insertions, deletions, or structural variations (gene or chromosome copy variations including aneuploidies) involved in drug resistance. At least two approaches can deliver suitable candidate nucleotide positions in genome-wide analysis. The first is inspired by human genetics and makes use of genome-wide association studies (GWAS). Quantitative trait loci (QTLs) analysis is also aimed at resolving the relationships between phenotypes and genotypes but not at the nucleotide resolution level of GWAS. The basic principle of GWAS is to screen diverse populations for phenotypic differences between individuals. Although many GWAS performed on fungal plant pathogens have focused on resistance to host factors [18], a few have addressed the identification of drug resistance genes with this approach. A landmark study was published by Mohd-Assaad and colleagues [19], in which azole resistance was investigated in Rhynchosporium commune, the causal agent of the barley scald disease. The study used whole-genome data from a set of 120 isolates and identified CYP51A mutations (the target of azoles) and several other loci (YVC1, a calcium channel, a transcription activator, and a saccharopine dehydrogenase) that also contributed to azole resistance. Another GWAS [20] on Fusarium graminearum (a fungus causing Fusarium head blight) mapped several phenotypes, including azole resistance. It is important to note that the genome data obtained here were from 119 isolates through restriction site-associated DNA sequencing (RADseq), which can provide a large number of SNP markers but at a lower resolution than whole-genome sequencing using high-throughput methods. The study identified 51 genes associated with propiconazole sensitivity in wild-type strains phenotyped under natural field conditions. This provided useful insights into non-CYP51A mediated resistance mediators. It is likely that other GWAS will emerge in the future; even if GWAS show great potential, they also have significant limitations. The structure of the investigated populations is of critical importance. For example, GWAS are more likely to succeed if (i) the investigated population is undergoing sexual recombination, (ii) the number of isolates originates from diverse conditions, and (iii) the number of investigated isolates is above a threshold of approximately 100 isolates [21]. Fungal species, including C. albicans [17] and C. glabrata [22] or filamentous fungi such as Aspergillus fumigatus [23], have marginal or absent sexual reproduction. Therefore, the power of GWAS is predicted to be limited in these clonal species. Despite this limitation, GWAS was successfully undertaken in a study of 387 C. neoformans var. grubii genomes and identified 10 loci associated with virulence capacity [24]. The study also investigated melanization in these isolates and identified a loss of function in BZP4, which is a transcription factor required to produce melanin, a molecule necessary for maintaining Cryptococcus virulence [24]. Another study performed on only 20 C. albicans isolates linked host-associated phenotypic traits with individual loci in the genome. A nonfilamentous isolate exhibited a nonsense mutation in EFG1, a gene involved in yeast-to-hyphae transition [25].

Finding the needle in a haystack

Genome-wide studies aimed to resolve drug resistance mechanisms on human pathogens are using in their majority genome comparisons between investigated isolates. The included isolates involve sometimes isogenic strains or strains with a high degree of relatedness. Studies enumerated in Table 1 give an overview of genome-based approaches available so far to understand drug resistance in fungal pathogens of clinical importance. A first aspect to consider is whether genomic data were obtained via de novo genome assemblies or through read alignments to reference genomes. De novo assemblies can capture genomic rearrangements and resolve individual gene copy number variations that could mediate drug resistance, which is beneficial for these purposes as compared to read alignments to reference genomes. On the other hand, whole chromosome or segmental aneuploidies can be the cause of drug resistance as reported in C. albicans [26] and C. neoformans [27]. These changes can be readily detected using read alignment strategies. Most of the genome data obtained in the studies from Table 1 are the results of read alignments, with the exception of specific C. auris and C. glabrata genome comparisons [28,29].

Given that genome comparisons yield in most cases a large number of SNPs and/or indels, a major challenge is to identify the nucleotide alterations associated with drug resistance. For example, in a study in which 17 related sequential C. albicans isolates with increasing azole resistance (over a 23-month period) were analysed, 4,610 nonsynonymous SNPs accumulated compared to the initial susceptible isolate [30]. Without expert eyes, it is a daunting task to deduce SNPs associated with drug resistance among other SNPs resulting from host adaptations or simply genetic hitchhiking. Comparisons with genes known to be involved in azole resistance is the approach of choice when attempting to detect SNPs relevant for drug resistance. Alternatively, traditional genetics may be helpful in this task: the work of Losada and colleagues [31] showed that it is possible to cross A. fumigatus in vitro to generate drug-resistant isolates with isogenic susceptible isolates of opposite mating type and then to select resulting drug resistant progenies for genome analysis. Therefore, the number of SNPs not relevant for drug resistance is reduced, speeding up the discovery of drug resistance associated SNPs. However, there are a number of genome studies in which the divergence between susceptible and resistant isolates is not as extensive, which facilitates the detection of SNPs associated with drug resistance. For example, Branco and colleagues [32] obtained two related C. parapsilosis isolates, one of which was azole-resistant, and identified two heterozygous SNPs and one homozygous SNP, the latter in a gene encoding the C-5 sterol desaturase (ERG3). The resulting nonsynonymous mutation yielded a nonfunctional Erg3 protein, which triggered azole resistance. Of note is that the azole-resistant isolate was generated in vitro thus shortcutting the accumulation of unrelated (i.e., host adaptation) mutations in the genome. On the other hand, the study published by Vale-Silva and colleagues [29] used two clinical C. glabrata isolates, one of which developed azole resistance in a patient within a 50-day time lapse. The azole-resistant isolate accumulated only 17 nonsynonymous mutations, among which one was in the regulator CgPDR1 known to mediate azole resistance. Very recently, comparative genome analysis helped to reveal an unexpected isolate diversity in C. lusitaniae isolates recovered from a single cystic fibrosis patient: the genomes of 20 different azole-resistant C. lusitaniae isolates from this patient revealed, among identified SNPs (24–131 between any two isolates) and indels (76–179 between any two isolates), accumulation of several distinct nonsynonymous SNPs in the transcription factor MRR1. This transcription factor is known to mediate azole resistance by upregulation of an efflux transporter [2], and therefore, the identified MRR1 SNPs were likely to explain the occurrence of azole resistance in this species [33].

Further steps

Other genome-wide analyses can be envisaged to correlate genotypes with phenotypes. One of them is the use of expression quantitative trait loci (eQTLs). eQTLs are naturally occurring genetic polymorphisms controlling gene expression levels; these analyses have proved to be a powerful approach for providing comprehensive data to interpret the link between genome variations and specific phenotypes [34]. This relatively novel approach has not yet been used in fungi-oriented studies; however, now that population-wide studies at the genome-scale are becoming popular, it promises to lead to interesting and novel findings.

In any of the discussed cases here, evidence is required to show the causal link between drug resistance and the candidate SNPs and/or indels identified by genome-wide studies. This supporting step has not been systematically addressed in the studies listed in Table 1. Nowadays, with the emerging technologies of gene editing (CRISPR [clustered regularly interspaced short palindromic repeats]-Cas9 [CRISPR-associated 9]), this task can be undertaken in several important fungal pathogens [35].

It is likely that in the future, more genome-based studies will emerge from fungal population analysis. Genome comparisons with known SNPs involved in drug resistance will still be the priority approach. Such comparisons may be facilitated by the creation of database resources, in which all known drug resistance genes and their allelic variants are deposited. This idea is currently attempted [36]; however, it should be strengthened by a strong commitment by the scientific community.

Acknowledgments

DS is thankful to Daniel Croll (University of Neuchâtel), Abhilash Kannan, Sanne Schrevens, and Alix Coste (University of Lausanne and University Hospital) for critical reading.

References

  1. 1. Fisher MC, Hawkins NJ, Sanglard D, Gurr SJ. Worldwide emergence of resistance to antifungal drugs challenges human health and food security. Science. American Association for the Advancement of Science; 2018;360: 739–742. pmid:29773744
  2. 2. Sanglard D. Emerging Threats in Antifungal-Resistant Fungal Pathogens. Front Med. 2016;3: 11. pmid:27014694
  3. 3. Lee AY, St Onge RP, Proctor MJ, Wallace IM, Nile AH, Spagnuolo PA, et al. Mapping the cellular response to small molecules using chemogenomic fitness signatures. Science. American Association for the Advancement of Science; 2014;344: 208–211. pmid:24723613
  4. 4. Hoepfner D, Helliwell SB, Sadlish H, Schuierer S, Filipuzzi I, Brachat S, et al. High-resolution chemical dissection of a model eukaryote reveals targets, pathways and gene functions. Microbiological Research. 2013. pmid:24360837
  5. 5. Xu D, Jiang B, Ketela T, Lemieux S, Veillette K, Martel N, et al. Genome-wide fitness test and mechanism-of-action studies of inhibitory compounds in Candida albicans. PLoS Pathog. 2007;3: e92. pmid:17604452
  6. 6. Schillig R, Morschhäuser J. Analysis of a fungus-specific transcription factor family, the Candida albicanszinc cluster proteins, by artificial activation. Mol Microbiol. 2013;89: 1003–1017. pmid:23844834
  7. 7. Schwarzmüller T, Ma B, Hiller E, Istel F, Tscherner M, Brunke S, et al. Systematic phenotyping of a large-scale Candida glabrata deletion collection reveals novel antifungal tolerance genes. Krysan DJ, editor. PLoS Pathog. 2014;10: e1004211–19. pmid:24945925
  8. 8. Jung K-W, Yang D-H, Maeng S, Lee K-T, So Y-S, Hong J, et al. Systematic functional profiling of transcription factor networks in Cryptococcus neoformans. Nature Commun. 2015;6: 6757. pmid:25849373
  9. 9. Forsburg SL. The art and design of genetic screens: yeast. Nat Rev Genet. Nature Publishing Group; 2001;2: 659–668. pmid:11533715
  10. 10. Heitman J, Movva NR, Hall MN. Targets for cell cycle arrest by the immunosuppressant rapamycin in yeast. Science. American Association for the Advancement of Science; 1991;253: 905–909.
  11. 11. Lynch M, Sung W, Morris K, Coffey N, Landry CR, Dopman EB, et al. A genome-wide view of the spectrum of spontaneous mutations in yeast. Proc Natl Acad Sci U S A. National Academy of Sciences; 2008;105: 9272–9277. pmid:18583475
  12. 12. Timmermann B, Jarolim S, Russmayer H, Kerick M, Michel S, Krüger A, et al. A new dominant peroxiredoxin allele identified by whole-genome re-sequencing of random mutagenized yeast causes oxidant-resistance and premature aging. Aging (Albany NY). 2010;2: 475–486. pmid:20729566
  13. 13. Ojini I, Gammie A. Rapid Identification of Chemoresistance Mechanisms Using Yeast DNA Mismatch Repair Mutants. G3 (Bethesda). Genetics Society of America; 2015;5: 1925–1935. pmid:26199284
  14. 14. Lang GI, Parsons L, Gammie AE. Mutation rates, spectra, and genome-wide distribution of spontaneous mutations in mismatch repair deficient yeast. G3 (Bethesda). Genetics Society of America; 2013;3: 1453–1465. pmid:23821616
  15. 15. Dorsaz S, Snäka T, Favre-Godal Q, Maudens P, Boulens N, Furrer P, et al. Identification and Mode of Action of a Plant Natural Product Targeting Human Fungal Pathogens. Antimicrob Agents Chemother. 2017;61: e00829–17. pmid:28674054
  16. 16. Peter J, De Chiara M, Friedrich A, Yue J-X, Pflieger D, Bergström A, et al. Genome evolution across 1,011 Saccharomyces cerevisiae isolates. Nature. Nature Publishing Group; 2018;556: 339–344. pmid:29643504
  17. 17. Ropars J, Maufrais C, Diogo D, Marcet-Houben M, Perin A, Sertour N, et al. Gene flow contributes to diversification of the major fungal pathogen Candida albicans. Nature Commun. Nature Publishing Group; 2018;9: 2253. pmid:29884848
  18. 18. Bartoli C, Roux F. Genome-Wide Association Studies In Plant Pathosystems: Toward an Ecological Genomics Approach. Front Plant Sci. Frontiers; 2017;8: 1165. pmid:28588588
  19. 19. Mohd-Assaad N, McDonald BA, Croll D. Multilocus resistance evolution to azole fungicides in fungal plant pathogen populations. Mol Ecol. 2016;25: 6124–6142. pmid:27859799
  20. 20. Genome-Wide Association Study Identifies Novel Candidate Genes for Aggressiveness, Deoxynivalenol Production, and Azole Sensitivity in Natural Field Populations of Fusarium graminearum. 2016;29: 417–430. pmid:26959837
  21. 21. Sánchez-Vallet A, Hartmann FE, Marcel TC, Croll D. Nature's genetic screens: using genome-wide association studies for effector discovery. Mol Plant Pathol. 2017;19: 3–6. pmid:29226559
  22. 22. Carreté L, Ksiezopolska E, Pegueroles C, Gómez-Molero E, Saus E, Iraola-Guzmán S, et al. Patterns of Genomic Variation in the Opportunistic Pathogen Candida glabrata Suggest the Existence of Mating and a Secondary Association with Humans. Current Biology. Elsevier Ltd; 2017: 1–21. pmid:29249661
  23. 23. Ashu EE, Hagen F, Chowdhary A, Meis JF, Xu J, Mitchell AP. Global Population Genetic Analysis of Aspergillus fumigatus. Mitchell AP, editor. mSphere. American Society for Microbiology Journals; 2017;2: e00019–17. pmid:28168221
  24. 24. Desjardins CA, Giamberardino C, Sykes SM, Yu C-H, Tenor JL, Chen Y, et al. Population genomics and the evolution of virulence in the fungal pathogen Cryptococcus neoformans. Genome Res. Cold Spring Harbor Lab; 2017;27: 1207–1219. pmid:28611159
  25. 25. Hirakawa MP, Martinez DA, Sakthikumar S, Anderson MZ, Berlin A, Gujja S, et al. Genetic and phenotypic intra-species variation in Candida albicans. Genome Res. Cold Spring Harbor Lab; 2015;25: 413–425. pmid:25504520
  26. 26. Selmecki A, Forche A, Berman J. Aneuploidy and isochromosome formation in drug-resistant Candida albicans. Science. 2006;313: 367–370. pmid:16857942
  27. 27. Sionov E, Lee H, Chang Y, Kwon-Chung K. Cryptococcus neoformans overcomes stress of azole drugs by formation of disomy in specific multiple chromosomes. PLoS Pathog. 2010;6: e1000848. pmid:20368972
  28. 28. Lockhart SR, Etienne KA, Vallabhaneni S, Farooqi J, Chowdhary A, Govender NP, et al. Simultaneous Emergence of Multidrug-Resistant Candida auris on 3 Continents Confirmed by Whole-Genome Sequencing and Epidemiological Analyses. Clin Infect Dis. 2017;64: 134–140. pmid:27988485
  29. 29. Vale-Silva L, Beaudoing E, Tran VDT, Sanglard D. Comparative Genomics of Two Sequential Candida glabrata Clinical Isolates. G3 (Bethesda). G3: Genes, Genomes, Genetics; 2017;7: g3.117.042887–2426. pmid:28663342
  30. 30. Ford CB, Funt JM, Abbey D, Issi L, Guiducci C, Martinez DA, et al. The evolution of drug resistance in clinical isolates of Candida albicans. Dermitzakis ET, editor. eLife. eLife Sciences Publications Limited; 2015;4: e00662. pmid:25646566
  31. 31. Losada L, Sugui JA, Eckhaus MA, Chang YC, Mounaud S, Figat A, et al. Genetic Analysis Using an Isogenic Mating Pair of Aspergillus fumigatus Identifies Azole Resistance Genes and Lack of MAT Locus’s Role in Virulence. Mitchell AP, editor. PLoS Pathog. 2015;11: e1004834–23. pmid:25909486
  32. 32. Branco J, Ola M, Silva RM, Fonseca E, Gomes NC, Martins-Cruz C, et al. Impact of ERG3 mutations and expression of ergosterol genes controlled by UPC2 and NDT80 in Candida parapsilosis azole resistance. Clin Microbiol Infect. 2017. pmid:28196695
  33. 33. Demers EG, Biermann AR, Masonjones S, Crocker AW, Ashare A, Stajich JE, et al. Evolution of drug resistance in an antifungal-naive chronic Candida lusitaniaeinfection. Proc Natl Acad Sci USA. 2018;32: 201807698. pmid:30389707
  34. 34. Nica AC, Dermitzakis ET. Expression quantitative trait loci: present and future. Philos Trans R Soc Lond, B, Biol Sci. The Royal Society; 2013;368: 20120362–20120362. pmid:23650636
  35. 35. Krappmann S. CRISPR-Cas9, the new kid on the block of fungal molecular biology. Med Mycol. 2017;55: 16–23. pmid:27811178
  36. 36. Nash A, Sewell T, Farrer RA, Abdolrasouli A, Shelton JMG, Fisher MC, et al. MARDy: Mycology Antifungal Resistance Database. Bioinformatics. 2018;484: 186. pmid:29897419
  37. 37. Vincent BM, Lancaster AK, Scherz-Shouval R, Whitesell L, Lindquist S. Fitness trade-offs restrict the evolution of resistance to amphotericin B. Mitchell AP, editor. PLoS Biol. 2013;11: e1001692. pmid:24204207
  38. 38. Garnaud C, Botterel F, Sertour N, Bougnoux M-E, Dannaoui É, Larrat S, et al. Next-generation sequencing offers new insights into the resistance of Candida spp. to echinocandins and azoles. J Antimicrob Chemother. 2015;70: 2556–2565. pmid:26017039
  39. 39. Castanheira M, Deshpande LM, Davis AP, Rhomberg PR, Pfaller MA. Monitoring Antifungal Resistance in a Global Collection of Invasive Yeasts and Molds: Application of CLSI Epidemiological Cutoff Values and Whole-Genome Sequencing Analysis for Detection of Azole Resistance in Candida albicans. Antimicrob Agents Chemother. American Society for Microbiology; 2017;61: e00906–17. pmid:28784671
  40. 40. Rhodes J, Abdolrasouli A, Farrer RA, Cuomo CA, Aanensen DM, Armstrong-James D, et al. Genomic epidemiology of the UK outbreak of the emerging human fungal pathogen Candida auris. Emerg Microbes Infect. Nature Publishing Group; 2018;7: 43. pmid:29593275
  41. 41. Escandón P, Chow NA, Caceres DH, Gade L, Berkow EL, Armstrong P, et al. Molecular epidemiology of Candida auris in Colombia reveals a highly-related, country-wide colonization with regional patterns in Amphotericin B resistance. Clin Infect Dis. 2018;13: e1006290. pmid:29788045
  42. 42. Singh-Babak SD, Babak T, Diezmann S, Hill JA, Xie JL, Chen Y-L, et al. Global analysis of the evolution and mechanism of echinocandin resistance in Candida glabrata. PLoS Pathog. 2012;8: e1002718. pmid:22615574
  43. 43. Biswas C, Chen SC-A, Halliday C, Kennedy K, Playford EG, Marriott DJ, et al. Identification of genetic markers of resistance to echinocandins, azoles and 5-fluorocytosine in Candida glabrata by next generation sequencing: a feasibility study. Clin Microbiol Infect. 2017. pmid:28344162
  44. 44. Salazar SB, Wang C, Musterkotter M, Okamoto M, Takahashi-Nakaguchi A, Chibana H, et al. Comparative genomic and transcriptomic analyses unveil novel features of azole resistance and adaptation to the human host in Candida glabrata. FEMS Yeast Res. 2017. pmid:29087506
  45. 45. Rybak JM, Dickens CM, Parker JE, Caudle K, Manigaba K, Whaley SG, et al. Loss of C-5 sterol desaturase activity results in increased resistance to azole and echinocandin antifungals in a clinical isolate of Candida parapsilosis. Antimicrob Agents Chemother. American Society for Microbiology; 2017: AAC.00651–17. pmid:28630186
  46. 46. Abdolrasouli A, Rhodes J, Beale MA, Hagen F, Rogers TR, Chowdhary A, et al. Genomic Context of Azole Resistance Mutations in Aspergillus fumigatus Determined Using Whole-Genome Sequencing. MBio. 2015;6: e00536–15–12. pmid:26037120
  47. 47. Ballard E, Melchers WJG, Zoll J, Brown AJP, Verweij PE, Warris A. In-host microevolution of Aspergillus fumigatus: A phenotypic and genotypic analysis. Fungal Genet Biol. 2018;113: 1–13. pmid:29477713
  48. 48. Camps SMT, Dutilh BE, Arendrup MC, Rijs AJMM, Snelders E, Huynen MA, et al. Discovery of a hapE Mutation That Causes Azole Resistance in Aspergillus fumigatus through Whole Genome Sequencing and Sexual Crossing. Fisher MMC, editor. PLoS ONE. Public Library of Science; 2012;7: e50034. pmid:23226235
  49. 49. Billmyre RB, Clancey SA, Heitman J. Natural mismatch repair mutations mediate phenotypic diversity and drug resistance in Cryptococcus deuterogattii. eLife. 2017;6. pmid:28948913