Unlocking the potential of experimental evolution to study drug resistance in pathogenic fungi

AbstractExploring the dynamics and molecular mechanisms of antimicrobial drug resistance provides critical insights for developing effective strategies to combat it. This review highlights the potential of experimental evolution methods to study resistance in pathogenic fungi, drawing on insights from bacteriology and innovative approaches in mycology. We emphasize the versatility of experimental evolution in replicating clinical and environmental scenarios and propose that incorporating evolutionary modelling can enhance our understanding of antifungal resistance evolution. We advocate for a broader application of experimental evolution in medical mycology to improve our still limited understanding of drug resistance in fungi.

IntroductionThe emergence and spread of antifungal resistance poses an imminent global health threat5. Critical in addressing this crisis is to investigate the mechanisms and dynamics of resistance. Traditionally, this can be done by tracking microbial population in patients over the course of treatment or by performing genome-wide association studies (GWAS) in which common variation in genomes of numerous clinical isolates is statistically linked to resistance. While such methods provide invaluable insights, they have the intrinsic limitations that variation emerged in a variable environment, is subject to genetic drift and host-pathogen adaptation and can therefore not always be attributed to drug resistance itself. In addition, GWAS studies heavily rely on big datasets and standardized data acquisition to be reliable, while serial clinical isolate studies have to deal with heterogeneous, polyclonal populations, and variable or limited sampling, often without a susceptible pre-treatment genotype. Random mutagenesis is another method to select for resistance, but has the risk of causing artificial and off-target effects. These issues complicate the identification of mutations associated with drug resistance6,7,8 and makes studying antifungal drug resistance with classical genomic approaches like “finding a needle in a haystack”, to quote Dr. D. Sanglard9.Experimental evolution (Box 1) offers a compelling alternative and complementary method to study drug resistance1. It mitigates the abovementioned problems, and allows for high repeatability and controlled, long-term monitoring of different strains and conditions to study resistance6. With experimental evolution, the relative effects of adaptation can be studied, allowing to quantify resistance and fitness trade-offs associated with it. Additionally, it creates the opportunity to study resistance from an eco-evolutionary perspective and assess the importance of differential mutation rates, drug and stress tolerance, spatiotemporal gradients, drug-drug, cell-cell interactions etc. Although experimental evolution provides valuable insights, it also has limitations, including an oversimplified environment that lacks the complexity of niche diversity, multiple stressors, host interactions and fluctuating conditions as found in the host. Furthermore, it is often focused on single-species populations and overlooks the role of microbial communities and immune responses. To overcome some of these simplifications, one option is to adopt setups such as fluctuating drug concentrations and co-culture models. Nevertheless, even “simple” in vitro experimental evolution of drug resistance can select for the acquired resistance mechanisms observed in clinical isolates10,11,12,13,14,15, highlighting the potential of this approach for studying antifungal drug resistance in pathogenic fungi.Box 1 Experimental evolution – seeing it happenExperimental evolution involves studying how organisms adapt to specific selective pressures in a controlled environment. By manipulating conditions and using selective regimes, naive strains are forced to develop a phenotype of interest. To study resistance development, isolates are grown in the presence of a drug and samples are taken at predetermined timepoints. This enables real-time monitoring of evolutionary changes and identification of genetic and/or phenotypic adaptations1,2, allowing for detailed observation of the speed and stepwise progression of drug resistance development over successive generations3,4.Measuring experimentally acquired resistance: a fitness trade-offIn medical mycology, fungal isolates are resistant when they exhibit a minimal inhibitory concentration (MIC) that exceeds a predetermined clinical breakpoint value16. This MIC is determined using standardized antifungal susceptibility testing (AFST) methods such as EUCAST16, CLSI17, or Etests18. Experimental evolution studies can deviate from this definition and approach, as they assess the relative decrease in drug susceptibility, which – in evolutionary terms – can be seen as increased fitness in the presence of a drug. Fitness refers in general to an organism’s ability to survive, grow, and reproduce in a given environment, and is therefore a highly complex ‘phenotype’19. This makes measuring fitness a challenging aspect inherent to experimental evolution studies.Fitness can be measured in various ways: from quantifying parameters such as growth rate or survival rate in specific conditions, to measuring competitive fitness of different strains in a co-culture (Fig. 1). Various techniques have been developed to accurately quantify subpopulation sizes in competitive fitness experiments. For example, strains can be labelled with a selective growth marker, enabling differentiation from unlabelled cell populations during CFU enumeration on selective agar media. Auxotrophic markers make strains dependent on external nutrients (e.g., amino acids) for differentiation20. Chemical resistance markers are genes that confer resistance to antimicrobial compounds like nourseothricin (NTC) or hygromycin B (HYG), enabling distinction based on growth in the presence of these specific chemicals21,22,23. Another way to label and differentiate strains is by incorporating fluorescent markers, such as green fluorescent protein (GFP) and red fluorescent protein (RFP), which allows real-time tracking and visualization of population dynamics using flow cytometry or fluorescence microscopy24,25. Alternatively, strains can be tagged by a unique DNA ‘barcode’ that allows high throughput quantification of subpopulation sizes through next-generation sequencing (NGS) of the barcodes26. Finally, once distinguishing polymorphisms between strains have been identified, markerless approaches such as quantitative PCR (qPCR) or deep sequencing can be employed. qPCR relatively quantifies population sizes by amplifying specific unique DNA sequences, such as resistance conferring mutations20,27, while deep sequencing involves NGS of these genetic markers with high coverage7,28.Fig. 1: Measuring competitive fitness.A An example of a competition experiment in which the population composition changes under the selective pressure of a drug. B Strains tagged with antimicrobial resistance markers like a nourseothricin (NTC) resistance gene (NAT), or hygromycin B (HYG) resistance gene (HPH) allow differential plating on NTC or HYG after competition. C Expression of fluorescent proteins like green fluorescent protein (GFP) or red fluorescent protein (RFP) enables discrimination between subpopulations using flow cytometry. D Barcode sequences in strains allow differentiation by next-generation sequencing (NGS). With deep sequencing (E) and qPCR (F), relative population sizes are estimated by the relative quantification of unique genetic markers, like antifungal drug resistance conferring mutations, based on NGS coverage or PCR amplification. Figure created with BioRender.com.Full size imageTraditional and emerging approaches for experimental evolution in pathogenic fungiHistorically, microbial genetic experiments focused on disrupting cellular functions to identify genes involved in biochemical and physiological processes. While this approach was productive for functional gene characterization, it did not address how these functions could improve. Innovations in the mid-20th century, such as continuous culturing methods and advanced molecular biology tools, combined with a paradigm shift in evolutionary biology from observational to experimental science, sparked the rise of experimental evolution29,30. This new approach allowed researchers to study the spontaneous accumulation of beneficial mutations in microorganisms, providing profound insights into functional enhancement and adaptation31.One of the first examples of experimental evolution in pathogenic fungi, was by Cowen and colleagues, whome exposed Candida albicans to fluconazole, discovering varying levels of resistance among the evolved isolates, each linked to distinct genetic changes in four key genes32. One year later, the same group mapped the fitness divergence of these fluconazole-resistant C. albicans isolates, demonstrating that resistance comes with a fitness cost, which could be mitigated by subsequent evolution in drug-free medium22. Some years later, and using experimentally evolved Aspergillus nidulans, Bruggeman et al.33 demonstrated that sexual reproduction in fungi can slow the accumulation of deleterious mutations compared to asexual reproduction.33 This study exemplified that fungi could serve as valuable models for exploring the evolutionary implications of sexual versus asexual reproduction on resistance development, an insight that can not be obtained in bacteria. In 2004, da Silva Ferreira et al. replicated susceptible Aspergillus fumigatus populations through transfers on agar medium with and without itraconazole, revealing varying levels of resistance, linked to different growth rates34. The study found that itraconazole resistance in A. fumigatus is linked to mutations in both the CYP51A and CYP51B genes, alongside the overexpression of multiple drug efflux transporters34. By 2005, Cowen and Lindquist identified the chaperone Hsp90 as a facilitator of resistance development to fluconazole in both C. albicans and Saccharomyces cerevisiae35. Furthermore, whole chromosome duplications and isochromosomes were identified as a mechanism for azole resistance in Candida species36,37,38,39.These early examples exploiting experimental evolution in fungi highlighted its potential to unravel crucial resistance mechanisms. Many research groups adopted and expanded upon these methods, applying experimental evolution in various approaches to study resistance. Table 1 and Fig. 2 provide an overview of commonly employed methods, along with their advantages and disadvantages. Since we aim to underscore the untapped potential of experimental evolution, it is beyond our scope to provide a full review of past studies. Many comprehensive reviews are already available on this topic1,2,3,4,7,31,40,41,42,43,44,45,46,47.Table 1 Methods of experimental evolutionFull size tableFig. 2: Methods of experimental evolution.A Serial dilution of the culture into fresh media, with at each step the possibility to adapt the drug concentration or create variable conditions. Various vessels can be used, including 96-well plates for large-scale replicates. B Morbidostats or chemostats provide a constant supply of fresh medium for continuous growth without imposing large population bottle necks (←→ serial dilution) and can adjust the drug concentration based on cell growth, measured for example by monitoring optical density (OD). C Spatial gradients can be created using agar plates with varying concentrations across the surface and inoculate the whole surface from the start or inoculate a part of the surface and rely on mobility of microbial cells to migrate across the surface. D Mathematical evolutionary modelling requires experimentally obtained parameters but simulates evolution in silico. E Different in vivo models, including Galleria mellonella and Caenorhabditis elegans can also be used for in vivo experimental evolution. Here shown are murine models of systemic, oropharyngeal and gastric infection, but murine models for skin and vagina, have also been used to study fungal infections. Figure created with BioRender.com.Full size imageBeyond classical methods in which the focus lies on investigating the molecular mechanisms of resistance81, innovative applications of experimental evolution have been employed to study other aspects of resistance such as evolutionary trends. For example, experimental evolution aided in the search for alternative therapies in pathogenic fungi by enabling the analysis of large numbers of resistant isolates, mapping trends in relative resistance and fitness trade-offs. Recently, serial batch transfer methods were employed to generate large numbers of resistant replicates, used to investigate the resistance dynamics of Candida glabrata61 and C. auris74,75. Ksiezopolska et al.61 demonstrated that (multidrug) resistance is often acquired at moderate fitness costs and mediated by mutations in a limited set of genes. Furthermore, they found that ERG3 mutations play a crucial role in cross-resistance to fluconazole in anidulafungin-resistant C. glabrata strains61. Carolus et al.75 mapped the susceptibility responses of experimentally evolved C. auris strains across diverse antifungals and found trends of collateral sensitivity, which is a phenomenon where resistance to one antimicrobial increases sensitivity to another. This is the opposite of cross-resistance, where resistance to multiple antimicrobials occurs simultaneously82,83. They illustrated the therapeutic potential of collateral sensitivity based alternative therapies to prevent and impede resistance in C. auris75. Another study used high throughput in vitro experimental evolution to map the diversity of acquired resistance mechanisms and associated fitness trade-offs of amphotericin B-resistance in C. auris74. In this study, fitness trade-off phenotyping and evolutionary modelling (see further) allowed to discover a fitness trade-off compensation mechanism, which might facilitate resistance development to amphotericin B74. Zhang et al. used agricultural triazoles, commonly applied for controlling fungal plant pathogens, to experimentally evolve A. fumigatus84. When testing these evolved strains, they found significant cross-resistance to clinically used azoles such as itraconazole, posaconazole, and voriconazole. Their findings highlight the impact of agricultural practices on medical resistance, underscoring the importance of adopting integrated strategies that link agricultural and medical antifungal use84.The question of how resistance evolves in the host remains critical, prompting the use of in vivo experimental evolution models. Andes et al.85 investigated the impact of fluconazole dosing strategies on resistance in C. albicans. They found that frequent dosing prevented the selection of resistant cells, while prolonged sub-MIC concentrations led to the growth of the resistant subpopulation85. Recently, in vivo experimental evolution in a systemic mouse model was used to obtain amphotericin B resistant C. auris strains, showing that even resistance mechanisms with severe fitness trade-offs86 can fixate in vivo74. Nevertheless, resistance development was relatively limited in the murine model compared to the in vitro parallel approach, likely due to lower drug concentrations and consequently reduced selective pressure in vivo, which may be insufficient for selecting resistant clones. Additionally, various in vivo factors, such as the host immune response and nutrient limitations, may further restrict resistance evolution, exposing limitations of the use of in vivo models74. Beyond animal models, in vivo experimental evolution has also been conducted in cell lines. For example, a recent study highlighted the importance of macrophage internalization in tolerance and later resistance development in C. glabrata. Drug tolerance was triggered by oxidative stress from macrophages, which facilitated the emergence of stable drug resistance80.Beyond resistance, in vivo experimental evolution has been employed to study other aspects of infections, such as virulence. Forche et al.77 examined how C. albicans survives as a commensal by exploiting the murine gastrointestinal tract as a model. They observed slower growth and higher rates of genomic and phenotypic variation in in vivo compared to in vitro populations77. Tso et al.79 went a step further and demonstrated that a pathogenic C. albicans strain could transition into a gut symbiont through passage in the mouse gut, partly by losing the ability to respond to hyphal-inducing factors79. Hilbert et al. used co-culture experiments to show that serial passaging of Cryptococcus neoformans through mammalian (macrophages) or environmental (amoeba) host cells, provides a competitive advantage in these host environments87. A single point mutation in CAC1, which encodes an adenylyl cyclase, was identified as the cause of this adaptive phenotype. However, the growth advantage in macrophages was inversely correlated with in vivo pathogenicity, as strains with this mutation led to reduced mortality in a murine infection model, highlighting possible trade-offs between niche adaptation and overall virulence87. Furthermore, Handelman et al. demonstrated that A. fumigatus can evolve resistance to high copper concentrations, stress encountered within phagosomes during infection88. They identified mutations in key genes such as PMA1, GCS1, and CPA1 in the evolved mutants, which enhance copper resistance and suggest a mechanism for evading immune responses88.It is important to note that experimental evolution in molds like Aspergillus spp. presents unique challenges compared to yeast-like fungi such as Candida spp. Molds require solid substrates and undergo complex life cycles, including spore formation and sexual reproduction, with each stage potentially responding differently to selective pressures. Accounting for both asexual and sexual cycles adds complexity, as recombination during sexual reproduction can increase genetic diversity and alter evolutionary paths. Additionally, the often longer generation times of molds compared to yeasts extend experiment durations and may delay the accumulation of adaptive mutations. Experimental evolution in yeasts is therefore more similar to that in bacteria.Extended use of experimental evolution: a glance at bacteriologyBeyond studying the molecular mechanisms of resistance, experimental evolution can be used to further explore the evolutionary dynamics of resistance evolution. In the field of bacteriology, many examples can be found in which innovative experimental evolution designs are used to answer more complex biological questions. These studies include examining the effect of metabolic state and drug tolerance, persister cells, genetic background, drug interactions, combination or cyclic treatments, and the spatial and temporal heterogeneity of drug exposure in evolution, to name a few. Additionally, the co-evolution of different strains and species in planktonic and biofilm communities have been investigated with experimental evolution in bacteria. Most of these topics might also be relevant to study in pathogenic fungi, in which similar methodologies could be applied. However, some constraints exist. For example, some molecular tools are not optimized for several species of pathogenic fungi, such as scarless and efficient genetic manipulation89,90,91,92,93,94. This can be partially addressed by using S. cerevisiae, which offers an extensive and highly optimized genetic toolbox for investigating antifungal drug resistance, similar to bacterial studies. Nevertheless, while S. cerevisiae is a valuable model for understanding fungal resistance mechanisms, the fact that it is not a pathogen must be considered95.Besides exposure to a single drug, bacterial experimental evolution has investigated combined and cyclic drug regimens to assess the impact of drug interactions (Fig. 3A)73. Synergy, where two drugs produce a combined effect greater than the sum of their individual effects, and antagonism, where one drug interferes with the action of another, are two important forms of drug interaction to consider in therapy (Fig. 3B)73. Intuitively, synergistic drugs are of great interest to increase treatment efficacy. However, Michel et al.96 demonstrated that such combinations might promote the evolution of resistance, while antagonistic combinations could reduce the range of drug concentrations that favour resistance selection 96. Additionally, studies with various drug combinations and mutants have shown that combination therapy can lower resistance rates, linked to epistatic interactions and fitness trade-offs96,97,98. For example, Suzuki et al.98 confirmed that certain antibiotic combinations reduce the development of resistance. Furthermore, they predicted antibiotic resistance by analysing gene expression profiles and applying a mathematical model, using only eight key genes whose expression shifts were strongly linked to resistance. This suggests that broad changes in gene expression, rather than specific mutations, can play a crucial role in resistance development, revealing common pathways that are central to acquiring resistance98,99.Fig. 3: Potentially important aspects and methodologies of experimental evolution.A Drugs can be added sequentially or simultaneously, as a variation on single drug exposure. Combination therapy allows the study of drug interactions (B), with lines representing growth isoboles. One drug concentration is linearly increased in each axis and the isoboles represent the combined drug effect where cell growth is inhibited. A straight isobole indicates no interaction, so an additive effect. If the drug combination achieves the same growth inhibition with a lower dose than the additive case, the combination is considered synergistic, indicated with convex isoboles. When the opposite is true, the drugs interact antagonistically and the isoboles are concave. C Representation of collateral sensitivity, where cells develop resistance to drug A, leading to increased susceptibility to drug B. D Example of hypothetical patient plasma concentrations of two drugs, which can be simulated in vitro using automated drug dosing in a bioreactor. E The range of effects caused by a mutation can be depicted as a distribution of fitness effects (DFE). Typically, most mutations have harmful effects and are quickly eliminated from the population. Mutations with neutral or nearly neutral impacts occur at a frequency resembling a clock-like distribution. Only a small number of mutations are beneficial. F A variant DNA library is introduced into cells of interest to create a mutant cell library. This library undergoes the treatment of interest, where cells with functional variants are enriched, while those with detrimental variants are depleted. The ratios of the genetic variants are determined before and after the drug exposure by deep sequencing to determine relative changes. Finally, the enrichment scores are analysed to assess the functional impact of the mutations. Figure created with BioRender.com.Full size imageCombining different drugs during evolution and mapping fitness trade-offs has sparked interest in collateral sensitivity (Fig. 3C). The term collateral sensitivity was coined in the pioneering study of Szybalski and Bryson, that showed that experimentally evolved Escherichia coli became more susceptible to some antibiotics when obtaining resistance to others, as a form of induced or “collateral” sensitivity100. Collateral sensitivity based therapy, in the form of either drug combinations or the cyclic administration of drug pairs that show collateral sensitivity can be of major therapeutic potential as they can be used to both prevent the emergence and fixation of resistant populations, and actively counteract resistance101. Imamovic et al.82 systematically mapped collateral sensitivity using experimental evolution of E. coli. They further demonstrated that collateral sensitivity based cycling therapy can prevent the selection for resistance. Lázár et al.102 used a similar approach, combined with high throughput sequencing to link collateral sensitivity to specific mutations that reduced efflux pump efficiency and led to hypersensitivity for several other antibiotics. Brepoels et al.103 found that the evolved genetic background significantly influenced the adaptive capacity of resistant strains, to develop resistance to a second drug. They reached this conclusion by exposing evolved resistant strains to a second drug in a new round of experimental evolution, with the antibiotic concentration normalized to the susceptibility of the resistant strain. They found that resistant cells were able to adapt better or worse compared to parental susceptible cells under the same selective pressure, by excluding the effect of collateral sensitivity and cross resistance, which highlights the importance of the genetic background in multidrug resistance development103. Vogwill et al.104 performed experimental evolution on various Pseudomonas strains and found that genetic diversity had little impact on the molecular mechanisms of resistance but played a significant role in the growth rate of the evolved resistant strains104. These studies show that collateral sensitivity and cross resistance are highly complex phenomena, most of which remain to be studied in pathogenic fungi. The recent study by Carolus et al.75 is among the first to systematically study collateral sensitivity in a pathogenic fungus and was inspired on collateral sensitivity studies in bacteriology.Another evolutionary aspect that has been considered in bacteria but hardly in fungi, is the relationship between the metabolic state and resistance evolution. The metabolic state refers to the overall activity and balance of metabolic pathways within a cell, and includes energy production, resource allocation, and biosynthetic processes which can critically influence the ability to withstand antibiotics in bacteria14,105. Lopatkin et al.14 showed that continuous antibiotic exposure typically selects for growth-dependent traits, without specific pressure on metabolic pathways, while experimental evolution with alternating periods of high antibiotic concentrations and drug-free conditions led to mutations in core metabolic genes that confered antibiotic resistance via altering metabolic pathways14. This approach allowed them to identify enriched processes and mutations linked to resistance within cellular metabolism, encompassing, but not limited to, glutamate synthesis, respiration, and the electron transport chain. Moreover, they confirmed the relevance of these processes in clinically resistant strains14. Previous research has shown that intermittent antibiotic exposure leads to the rapid evolution of tolerance106, refering to the ability of drug susceptible cells to survive and grow slowly in drug concentrations exceeding the MIC by relying on diverse stress pathway responses107. Levin-Reisman et al.106 used experimental evolution in three steps - antibiotic treatment, washing off the antibiotic and drug-free growth - to select for tolerant rather than resistant cells. By further analysing the evolved strains, they found that the development of resistance was preceded by a tolerant state106. When only a small fraction of the population switches to the tolerant state, this is reffered to as persistence108. Experimental evolution studies with E. coli demonstrated that persister cells can rapidly emerge after the onset of treatment and allow for extremely high levels of drug tolerance during prolonged treatment108. Furthermore, Van den Bergh et al. demonstrated that persistence significantly contributes to the evolutionary response to antibiotics, creating a reservoir for resistance evolution within the population108. These studies indicate that drug tolerance and persistence are important aspects in resistance development, and while these phenotypes have been identified in fungi107, their role in resistance development remains poorly understood, creating significant untapped potential for experimental evolution research.To explore resistance evolution in more complex environments, applying spatiotemporal heterogenity to the evolving cells can provide insights into important mechanisms and dynamics. A famous example to study spatial gradients, is the ‘microbial evolution and growth arena’ or MEGA plate technique109, in which bacteria can spread across a large landscape with varying antibiotic concentrations. A great advantage of this approach is that mutation and selection in the migrating bacterial front can be directly visualized over time109. Another way to gain insights into resistance dynamics in changing environments is to vary drug exposure over time. Bioreactor systems are ideal for this purpose. The drug concentration in the system can be gradually increased110 or adapted to mimick pharmacodynamic effects111 or control the population size70, ensuring a constant selective pressure (Fig. 3D). Feng et al.111 used this approach by exposing Pseudomonas aeruginosa to drug conditions that simulate in-patient plasma concentrations during treatment. They demonstrated that resistance development during treatment can be explained by de novo acquisition of resistance, without necessarily involving genetic exchange of resistance genes111. In pathogenic fungi, the importance of drug pharmacodynamics or other spatiotemporal aspects of resistance evolution remains to be studied.Another important factor to mimic real-life conditions is co-evolution of cell-cell interactions within the structured and diverse communities micro-organisms often live in112. In a recent study, co-evolution of the soil bacterium Bacillus subtilis and the fungus Aspergillus niger was investigated113. The researchers found that B. subtilis increased its surfactin production in co-culture, inhibiting fungal growth and facilitating its own competitive success113. This study highlights the potential of combining co-culture and laboratory evolution experiments to enhance our understanding of bacterial-fungal interactions113. Another interesting combination of both spatial heterogenity and multispecies interactions are multi-species biolfilms. These are complex, multicellular communities encased in an extracellular matrix, providing a protective environment that can enhance resistance to antimicrobial drugs40,114. Within these biofilms, various biofilm-specific resistance mechanisms are at play, such as reduced antibiotic penetration, altered microenvironments that affect antibiotic efficacy, and the formation of persister cells that can survive antibiotic treatment and repopulate after treatment40,115. A recent study by Usui et al.116 demonstrated that E. coli biofilms exposed to antibiotic cycles rapidly evolved resistance, unlike planktonic cells. Mixed biofilm models have demonstrated the importance of C. albicans cell wall polysaccharides in the tolerance of Staphylococcus aureus to antibiotics117. The significance of this interaction has also been shown in vivo, where mice with oral candidiasis experienced severe systemic S. aureus infections with high mortality and morbidity following subsequent exposure to the bacteria118. Whether biofilm-mediated resistance and polymicrobial interactions contribute to antifungal drug resistance and treatment failure remains however to be investigated.In bacteriology, the relationship of resistance and multifaceted fitness has been studied in depth. By mapping the fitness of a pool of experimentally evolved clones in diverse environments, fitness landscapes can be mapped and compared between strains, species and in different (drug) conditions. Fitness landscapes enable to predict how resistance will evolve and can help to identify key mutations that allow micro-organisms to stay fit or virulent while becoming resistant119,120. Plotting the distribution of fitness effects (DFE), involves systematic screening of resistance-fitness relationships to map the frequency of advantageous, neutral, or deleterious mutations after experimental evolution (Fig. 3E). The position of a mutation within the DFE determines the likelihood of a genotype’s ability to evolve and fixate121. Chevereau et al.122 mapped the DFE for drug resistance across eight antibiotics in E. coli, providing insights into the highly variable fitness landscapes and resistance dynamics for each drug. Similarly, Papkou et al.120 created a detailed resistance-fitness landscape for E. coli, revealing that while resistance often involves fitness trade-offs, multiple accessible evolutionary paths allow populations to reach high-fitness states. Recently, fitness trade-off compensation was identified in C. auris too74, although it was not discovered by mapping DFE. A comparative DFE study evaluated how bacterial transformation, a common horizontal gene transfer mechanism, can accelerate adaptive evolution123. Hybrid libraries were created by combining the recipient B. subtilis with various donor species and their fitness was assessed under different conditions. They found that certain gene transfers were only beneficial in a single growth condition, indicating that recipient strains can benfit from diverse donor genes to adapt to varying conditions. The study concluded that related species utilize a shared gene pool, facilitating rapid adaptation to changing environments through transformation123. Although several recent studies have mapped resistance-fitness associations in pathogenic fungi61,74, systematic mapping of the DFE of resistance to antifungal drugs remains unexplored territory, with significant potential.The evolution of drug resistance can also be ‘forced’ without relying on the selection of stochastically occurring mutants, by using techniques such as random and site-directed mutagenesis. These methods increase the mutational supply and introduce genetic variation before applying selective pressure, followed by the analysis of population structure after selection124. Site-directed mutagenesis can for example be performed by a transformation with diversified PCR products to create variation in a gene of interest125. This method was used to introduce FKS1 mutations in S. cerevisiae and allowed to identify a third ‘hot spot’ region in the gene, important for resistance against echinocandins67,125. Deep mutational scanning (DMS), is another high throughput technique to systematically study the effects on fitness and/or resistance of all possible substitutions in a locus of interest124 (Fig. 3F). It involves creating a library of variants, often with a high throughput CRISPR approach, and performing competitive fitness assays followed by deep sequencing of the pool before and after selection. This method is particularly useful for understanding resistance mechanisms linked to mutations in a specific gene, such as a drug target124. With DMS, detailed fitness landscapes can be created, illustrating a protein’s evolutionary potential and constraints. For example, Dewachter et al.126 applied DMS to create a comprehensive map of three essential bacterial proteins. The study demonstrated that clinically relevant mutations in these proteins could confer antibiotic resistance. By predicting which mutations are likely to arise and confer resistance, they provided recommendations for antibiotic development that account for the ease of resistance evolution in the considered target proteins126. In fungi, DMS has been succesfully applied in S. cerevisiae to study variation in ERG11127, the target of azoles, FKS1128, the target of echincoandins, and FCY1129, an enzyme involved in 5-fluorocytosine activity. Recently, the same approach was applied to identify mutations conferring resistance to methotrexate in the dihydrofolate reductase of the fungal pathogen Pneumocystis jirovecii28. A comprehensive catalogue of resistance mutations was created, allowing to elucidate their relationship with enzyme function and fitness trade-offs. Although mutagenesis approaches and DMS mapping significantly differ from classical experimental evolution, they do not rely on the emergence of random genetic variants, but create them before selection is applied. Nevertheless, lots can be learned from these approaches and analytic techniques. Future advancements, such as combining pooled experimental evolution with lineage tracing, could significantly increase throughput and refine the mapping of resistance or multidrug-resistance phenotypes130. The fitness-resistance landscape of most antifungal drug targets and other genes involved in resistance remains to be studied in fungi, and creates a vast opportunity to identify superior drugs or analogues and predict treatment outcomes based on specific variation.Modelling evolution: in silico experimental evolution approachesThe experimental study of resistance evolution is complex, particularly due to the intrinsic stochasticity of the evolutionary process. Each evolution experiment can generate distinct outcomes because of the random occurrence of mutations that drive evolutionary dynamics41,122. As a consequence, numerous replicates are required to accurately estimate the evolutionary outcome. Additionally, screening for variation in evolution experiments is complicated due to factors like diversification and clonal interference, where multiple mutants coexist simultaneously, or epistasis, where the effect of one mutation is influenced by the presence of others131,132. Furthermore, the impact of mutations can vary across different environments133, making the choice for environmental parameters extremely important in the interpretation of the experimental results. Large-scale experimental evolution efforts are limited by practical constraints like the number of repetitions and testable variables, the simplicity of the most often in vitro environment and practical limitations in screening for variation. Mathematical evolutionary models provide however a valuable and complementary tool for exploiting this evolutionary complexity. They allow for the generation of both qualitative and quantitative data and hypotheses, can provide guardrails for designing experimental setups and can help explain experimental results. Within the field of evolutionary modelling of antimicrobial drug resistance, different approaches exist, each with a unique framework41,134.Deterministic models are fundamental in mathematical modelling of evolutionary processes. They provide a structured approach for understanding the behavior of large microbial populations under selective pressures. These models employ systems of ordinary differential equations (ODE) to describe the continuous change in population sizes and/or allele frequencies over time131,135. The equations account for key processes such as density-dependent population growth, drug-related death, and external death rates (e.g., due to immune killing)76,131,136. A hypothetical example of a model that predicts the population dynamics of different mutants based on their drug resistance (with MIC as a proxy for resistance) and fitness (with growth rate as a proxy for fitness) is depicted in Fig. 4, which was inspired by literature74,76,136. The change in drug concentration over time is modelled using a pharmacokinetic (PK) function, representing typical exponential decay. The drug-induced killing rate is determined jointly by the antibiotic concentration and a pharmacodynamic (PD) dose-response curve, which describes the drug’s impact on cellular growth and survival. In this example, the immune response is given by a constant, but various equations can be used to model dynamic immune effector population137.Fig. 4: Example of a hypothetical deterministic modelling framework for predicting the population size of strains based on their fitness-resistance profile in a ‘virtual patient’ after x days of treatment.A The ordinary differential equations (ODE) for the susceptible (S) and resistant (R) cell population over time (t) form the basis of the deterministic model and account for cell growth, drug effects, and immune response (indicated by different colours in the equation). Only the ODE of the resistant cell population is shown for simplicity. Susceptibility (MIC) and growth parameters (g, K, F) are detailed in (B) and (C), determined by respectively the dose response curve and the growth curve of the mutants. The drug concentration depends on drug decay in the patient (h), shown in (D). In this example, exponential decay is assumed, and h is dependent on the half-life of the drug. The drug induced killing is determined by the Hill coefficient (H), indicated by a varying steepness of the dose response curve, shown in (E). The immune effect is simplified as one variable (I) for simplicity. F Hypothetical modelling output depicting the relationship between fitness-resistance profile of three hypothetical mutants (coloured dots) and the predicted population sizes (colour of heatmap) of these three mutants after x days of treatment in a ‘virtual patient’. In this case, only the mutant with low resistance and a medium fitness trade-off is expected to survive treatment, while susceptible or highly resistant but less fit mutants are predicted to go extinct. Figure created with BioRender.com.Full size imageDeterministic models can provide insights into the virulence and survival capability of resistant mutants during in vivo treatment that would be difficult to obtain experimentally. In the example in Fig. 4, which assumes a trade-off between resistance and fitness, only the moderately resistant mutant is likely to fixate during infection, while susceptible and highly resistant but less fit mutants go extinct due to both drug and immune cell related killing74. If a model is well-parameterized and validated, it can be used to provide probability estimates of resistance development under different treatment strategies, something that would be virtually impossible to achieve with in vivo or in vitro methods in clinically relevant settings. Evolutionary models can be flexibly adapted to provide both qualitative and quantitative predictions in a variety of contexts, including studying the effect of combination therapy and drug cycling (compared to monotherapy) and assessing the impact of collateral sensitivity and/or cross-resistance on infection dynamics. Additionally, such models can provide a rationale to efficiently set up in vivo experiments for validation, curtailing both time and monetary expenses, and minimizing the use of animals.In recent work, deterministic models were used to gain insights into the population dynamics of antifungal drug resistance of C. auris, based on insights from experimental evolution. It was found that only mutants with moderate resistance and a limited fitness loss are expected to cause an infection during amphotericin B treatment, and that fitness compensation mechanisms can increase this likelihood74. To validate the efficacy of collateral sensitivity based drug cycling in preventing resistance development, another study employed a similar deterministic model to investigate the likelihood of resistance emergence in C. auris during cycling of the collateral sensitivity drug pair amphotericin B and caspofungin in a virtual patient. This model predicted that the shorter the cycling intervals were, the lower the chance was for resistance to amphotericin B or caspofungin to establish75.Unlike deterministic models, which describe the average behavior of large populations, stochastic models explicitly account for the role of random events135,138 and can provide an understanding of how different outcomes may arise from the same initial conditions138,139. Stochastic events can significantly influence evolutionary outcomes at the early stages of selection, especially when there are significant bottlenecks or small populations139,140. Stochastic models simulate population dynamics by modelling reproduction, death, and mutation as probabilistic events. Techniques such as Gillespie algorithms141 and Markov processes142 enable simulations that incorporate randomness at the level of individual microorganisms. Each individual may reproduce, die, or mutate with set probabilities and the drug concentration affects the probability of death, while mutations, potentially conferring resistance, occur randomly during reproduction. Stochastic models integrate these events to provide a population state at each time step, capturing the randomness of resistance evolution.Beyond simulations, stochastic models also serve as analytical tools. The probability theory, Markov processes and differential equations can be used to calculate parameters like expected population size or extinction probability by a certain time143,144. For instance, a study by Czuppon et al.139 combined simulations with analytical predictions to assess the survival probability of a resistant bacterial subpopulation. The study examined how antibiotic dose, mode of action (biostatic vs. biocidal), and bacterial competition influence the evolution of resistance. They showed that population density and drug mode significantly impact the survival of the resistant subpopulation. Specifically, biocidal antibiotics quickly reduce susceptible bacteria, increasing the survival chances for resistant strains through competitive release. In contrast, biostatic drugs can slow the emergence of resistance by preserving competition between susceptible and resistant cells139. The study also highlighted that intermediate antibiotic concentrations maximize resistant strain survival, as these levels neither fully eliminate the susceptible population nor outcompete the resistant strains139. In the future, combining such mathematical models with experimental evolution or competition experiments could validate these predictions and offer insights in optimizing drug dosages to reduce resistance development.Although modelling can significantly enrich evolutionary studies, it can also have its limitations. Deterministic models assume large populations where random effects are negligible, which may not be true in early resistance development or small, isolated populations. Stochastic models, on the other hand, face computational complexity and require extensive simulations, especially for large populations. To address these issues, researchers use hybrid models that combine deterministic and stochastic elements. These models use deterministic equations for large population dynamics and stochastic simulations for small populations or specific events. For instance, a hybrid model might apply deterministic equations for overall population growth while using stochastic simulations to capture random mutations75,76. Despite their potential, evolutionary modelling approaches are still rarely integrated into antifungal drug resistance research and mycology in general, highlighting an area for future development.ConclusionsBy simulating drug pressures in controlled environments, experimental evolution offers insights into the mechanisms of resistance and the evolutionary trajectories under drug selection. Rather recently, it has revealed critical aspects of resistance such as the importance of drug interactions, metabolic states, spatiotemporal heterogenity, ecological interactions and trade-offs like collateral sensitivity to name a few, which traditional approaches might overlook. Given the limited number of antifungal drugs and the stagnation of the antifungal drug development pipeline, the lessons learned from experimental evolution might be essential for developing innovative therapeutic strategies to fight the growing antifungal drug resistance problem. In this review, we highlighted the significant potential of experimental evolution as a tool for understanding and combating the antifungal drug resistance crisis.The fields of bacteriology and evolutionary modelling provide excellent examples of how to design more complicated experimental set-ups and perform complex analyses to address advanced research questions. Overall, experimental evolution in medical mycology has been used to address rather straightforward aspects of resistance, such as the molecular mechanisms that confer it, underscoring the potential for using advanced methods of experimental evolution in future research. By expanding the application of these methodologies and integrating them with genomic and computational tools, we can enhance our predictive capabilities and devise strategies that not only manage but also preempt the emergence of resistance. With the escalating crisis of antifungal resistance representing one of the most daunting challenges in contemporary medical mycology, let this review serve as a call to arms to embrace and innovate upon experimental evolution techniques in the fight against antifungal resistance.

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Download referencesAcknowledgementsThis work was supported by the Fund for Scientific Research Flanders (FWO) under the framework of the JPIAMR – Joint Programming Initiative on Antimicrobial Resistance fund (G0L1622N), and by a C3 grant from the Industrial Research Fund of KU Leuven (C3/22/007) granted to P.V.D., P.v.d.B. acknowledges the support of KU Leuven start-up funding (GNM-E1097-STG/21/003). S.J. and G.B. were supported by FWO PhD fellowships 11PRR24N and 1150023 N respectively. H.C. was supported by a post-doctoral fellowship granted by KU Leuven Internal Funds (PDMT2/23/032).Author informationAuthors and AffiliationsLaboratory of Molecular Cell Biology, Department of Biology, KU Leuven, Leuven, BelgiumStef Jacobs, Patrick Van Dijck & Hans CarolusEvolutionary Modelling Group, Department of Biology, KU Leuven, Leuven, BelgiumGiorgio Boccarella & Pieter van den BergEvolutionary Modelling Group, Department of Microbial and Molecular Systems, KU Leuven, Leuven, BelgiumPieter van den BergKU Leuven One Health Institute, KU Leuven, Leuven, BelgiumPatrick Van DijckAuthorsStef JacobsView author publicationsYou can also search for this author in

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PubMed Google ScholarContributionsS.J.: writing, editing and illustration design. G.B.: review and editing. P.v.d.B. and P.V.D.: review and editing, supervision, funding acquisition. H.C.: conceptualisation, writing, review, editing, illustration design, supervision, funding acquisition.Corresponding authorCorrespondence to

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Reprints and permissionsAbout this articleCite this articleJacobs, S., Boccarella, G., van den Berg, P. et al. Unlocking the potential of experimental evolution to study drug resistance in pathogenic fungi.

npj Antimicrob Resist 2, 48 (2024). https://doi.org/10.1038/s44259-024-00064-1Download citationReceived: 26 June 2024Accepted: 15 November 2024Published: 12 December 2024DOI: https://doi.org/10.1038/s44259-024-00064-1Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard

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