Azole-Driven Cross-Resistance and Transporter Gene Expression in Malassezia Yeasts

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The study demonstrates that ketoconazole treatment induces persistent, irreversible cross-resistance to other antifungals in Malassezia furfur. While previous studies have shown that azole exposure can induce cross-resistance in various fungal species, the specific finding that ketoconazole induces a persistent elevation in MICs that does not decrease after drug removal in Malassezia furfur is a novel contribution. 

The paper elucidates the distinct temporal expression dynamics of secondary MFS transporters OPT1 and FLR1 during azole exposure.Although the upregulation of MFS transporters like OPT1 and FLR1 upon azole exposure was previously noted, their specific functional dynamics over extended exposure to different azoles were not well characterized in Malassezia. By tracking their expression profiles over a four-week period and subsequent drug removal, the study provides novel insights into the temporal dynamics of secondary transporters in adaptive resistance.

The study utilizes gene interaction network analysis from model yeasts to map uncharacterized efflux pathways in Malassezia. Malassezia species lack well-annotated gene interaction databases, making it difficult to understand the functional relationships between resistance genes. By leveraging network data from model yeasts like S. cerevisiae and S. pombe to map the interactions of PDR10, FLR1, OPT1, and CAF5, the authors provide a novel framework for understanding the systems-level organization of  resistance in this genus. 

Major concerns

1)  Azole doses not normalized to MIC. The paper's comparative conclusions across three azoles are undermined by the use of non-equivalent selection pressures, with treatment concentrations differing by ~67-fold without anchoring to each drug's MIC fraction. Since ketoconazole, clotrimazole, and fluconazole were applied at arbitrary absolute concentrations rather than equivalent fractions of their baseline MICs, the stronger and more persistent cross-resistance observed with ketoconazole in Figures 1C and 2 could reflect proportionally higher selection pressure rather than intrinsic drug properties. The failure to normalize treatment concentrations to equivalent MIC fractions represents a methodological flaw.

2) No direct efflux or drug accumulation assay. The paper's central mechanistic claim of efflux-mediated cross-resistance is supported only by correlation between transcript levels and MIC changes, with no direct measurement of drug efflux, intracellular drug accumulation, or transporter protein levels. Without efflux assays or protein-level validation, the observed MIC increases in Figure 1 could equally result from target site mutations in CYP51, membrane composition changes, or stress response activation, rather than the efflux mechanism invoked in the title. The expression correlations in Figures 2 and 3 and the knockout phenotype are consistent with efflux but do not exclude these alternatives. Perform these experiments or justify or town down the title and conclusions by contextualizing the mechanistic evidence

3) Ketoconazole treatment causes persistent elevated MICs in all tested antifungals that do not decrease after drug removal.  Persistence is inferred from the observation that MICs remain elevated for two weeks after ketoconazole withdrawal in the time-course data. The timeline figure specifies a four-week exposure followed by only two weeks without drug before the final MIC measurement. While the pattern is compelling within this window, no longer drug-free passages or stability assays are shown (see Figure 1A and Figure 1C).. With only two weeks of washout (Figure 1A, Figure 1C), the data cannot distinguish reversible tolerance (e.g., epigenetic or metabolic adaptation) from durable resistance; longer passages or reversion tests would be needed to substantiate 'persistence'. Clinical and experimental studies demonstrate that resistance reversion occurs over weeks to years depending on fitness costs and compensatory mutations, with some reversible adaptations persisting for extended periods before reversion. Moreover, transporter expression measurements decline after withdrawal in related figures, suggesting potential decoupling from long-term stability and underscoring the need for extended follow-up or genetic analyses. Justify the current findings by clarifying the timeframe

4) Absolute MIC values, variance measures, and statistical tests supporting post-withdrawal persistence for each drug are not reported. Without raw MICs and variability, it is difficult to judge the magnitude and clinical relevance of the observed increases or to verify that post-withdrawal values are significantly different from baseline for all eight drugs (Figure 1C).

Minor concerns:

1) The PDR10 transcript time-course is presented as a bar plot normalized to ACT1, (Figure 2). Normalization relies on a single housekeeping gene (ACT1) without demonstrated stability under prolonged azole exposure. For example, research in Candida glabrata shows ACT1 is dramatically upregulated (4.5-32.7-fold) by azole treatment, making it unsuitable for qPCR normalization under azole stress (10.1186/1471-2199-13-22) If ACT1 transcription varies with chronic azole treatment, relative normalization could misestimate PDR10 fold changes in Figure 2.

2) The evidence for azole-induced PDR10 upregulation is derived from RT-qPCR fold changes displayed in Figure 2, with clear increases by week 4. Primer efficiency and amplification performance for the PDR10 qPCR assay (standard curves, efficiency values, melt-curve specificity) are not reported.

3) PDR10 is a key mediator of reduced susceptibility to azoles and knockout of the PDR10 gene prevents acquisition of additional resistance. The conclusion that loss of PDR10 prevents acquisition of additional resistance rests on the observed lack of MIC increases in the knockout under clotrimazole and fluconazole and its failure to survive ketoconazole beyond two weeks, as described in the Results text. This interpretation is bolstered by the robust induction of PDR10 during azole exposure (Figure 2) and the clear acquisition of cross-resistance in the wild-type background (Figure 1). These findings are compelling, but the study does not present the construction details or validation of the PDR10 deletion strain. The study does not provide experimental validation of the PDR10 deletion (genotype and loss of transcript/protein) or a genetic complementation rescue to confirm on-target causality. Justify the current findings by linking existing results to necessity under the tested regimen

 

 

 

 

Author Response

Reviewer 1:

 

Major concerns

• Azole doses not normalized to MIC. The paper's comparative conclusions across three azoles are undermined by the use of non-equivalent selection pressures, with treatment concentrations differing by ~67-fold without anchoring to each drug's MIC fraction. Since ketoconazole, clotrimazole, and fluconazole were applied at arbitrary absolute concentrations rather than equivalent fractions of their baseline MICs, the stronger and more persistent cross-resistance observed with ketoconazole in Figures 1C and 2 could reflect proportionally higher selection pressure rather than intrinsic drug properties. The failure to normalize treatment concentrations to equivalent MIC fractions represents a methodological flaw.

We apologize for not specifying clearly in the manuscript, the rationale behind the choice of treatment concentrations and the confusion in the use of the term ‘sub-MIC’. To clarify, the MIC values used in this study were two to four steps higher than the known MIC for the specific as previously published (Leong et al 2019, see full reference below). Thus, this should be interpreted as a high stress dose condition. This was based on our observations from previously published studies where we saw an increase in clotrimazole MICs after 4 weeks of successive treatment in a dose dependant manner. The phenomenon was best observed at antifungal concentrations two to four steps higher than the known MIC for the specific strain. 

 

For this study, the respective concentrations of each antifungal were the highest tolerated for CBS 7982. Higher concentrations tested were less well tolerated over long-term culture and the low inoculum yield impeded further susceptibility testing. It would be important to note that mDixon media was used for long term culture (vs OptiMAL aka RPMI with supplements for MIC testing). Given that mDixon is nutrient rich, we would expect that they would be able to tolerate higher than MIC doses up to a certain degree (vs the MIC measurement growth conditions).

 

We have reworked the methods, results and discussion to clarify and address the underlying rationale for this choice of antifungal dosage.

 

Leong C, Kit JCW, Lee SM, Lam YI, Goh JPZ, Ianiri G, Dawson TL Jr. Azole resistance mechanisms in pathogenic M. furfur. Antimicrob Agents Chemother. 2021 May 1;65(5):e01975-20. doi: 10.1128/AAC.01975-20. Epub 2021 Feb 22. PMID: 33619053; PMCID: PMC8092866.

 

 

 

2) No direct efflux or drug accumulation assay. The paper's central mechanistic claim of efflux-mediated cross-resistance is supported only by correlation between transcript levels and MIC changes, with no direct measurement of drug efflux, intracellular drug accumulation, or transporter protein levels. Without efflux assays or protein-level validation, the observed MIC increases in Figure 1 could equally result from target site mutations in CYP51, membrane composition changes, or stress response activation, rather than the efflux mechanism invoked in the title. The expression correlations in Figures 2 and 3 and the knockout phenotype are consistent with efflux but do not exclude these alternatives. Perform these experiments or justify or town down the title and conclusions by contextualizing the mechanistic evidence.

We agree with the reviewer’s assessment that demonstration of drug efflux is necessary to claim mechanistic evidence efflux mediated cross resistance and thank the reviewers for pointing this out. To address this, we have performed additional experiments using the fluorescent dye, rhodamine 6G to assess overall cell efflux activity after azole treatment as below. The below methods and information have been included in the main manuscript text and supplementary figures accordingly.

Briefly, cells were treated with the respective concentrations of ketoconazole, clotrimazole and fluconazole at 32⁰C. After 1 week, cells were pellet and washed 3x in PBS followed by incubation with 10 µM of Rhodamine 6G in PBS (glucose starvation) for 30 mins. This was followed by washing twice in ice cold PBS. Lastly, cells were resuspended in PBS with 2% glucose and incubated at 32⁰C, cell aliquots were harvested at 0, 5, 15 and 30 min intervals and the supernatants collected for measurement on a fluorescence plate reader (Excitation/Emission: 525nm/550nm).

We did not observe any appreciable increase in rhodamine 6G efflux after treatment (See below - Supplementary Figure 2).

The PDR10∆ knockout strain did show elevated levels of efflux compared to CBS 7982 strains (treated and untreated). However, this may be an intrinsic phenotype of the strain (its parent strain also has elevated rhodamine 6G levels, (see Figure 6b below from Leong et al 2019). A limitation of this assay is that it requires cell to be metabolically active for measurement of efflux (i.e. log phase). Cells that have been treated with antifungal for extended duration may no longer be sufficiently metabolically active for this assay to be an accurate measure of drug efflux. As per the reviewers’ recommendations, we have scaled down our claims for the role of efflux pump activity in the text and addressed these limitations in the discussion.

3) Ketoconazole treatment causes persistent elevated MICs in all tested antifungals that do not decrease after drug removal.  Persistence is inferred from the observation that MICs remain elevated for two weeks after ketoconazole withdrawal in the time-course data. The timeline figure specifies a four-week exposure followed by only two weeks without drug before the final MIC measurement. While the pattern is compelling within this window, no longer drug-free passages or stability assays are shown (see Figure 1A and Figure 1C). With only two weeks of washout (Figure 1A, Figure 1C), the data cannot distinguish reversible tolerance (e.g., epigenetic or metabolic adaptation) from durable resistance; longer passages or reversion tests would be needed to substantiate 'persistence'. Clinical and experimental studies demonstrate that resistance reversion occurs over weeks to years depending on fitness costs and compensatory mutations, with some reversible adaptations persisting for extended periods before reversion. Moreover, transporter expression measurements decline after withdrawal in related figures, suggesting potential decoupling from long-term stability and underscoring the need for extended follow-up or genetic analyses. Justify the current findings by clarifying the timeframe

Thank you for the comment. We only claim to see elevated MICs after a defined two-week drug-free interval. Our design included four weeks of continuous ketoconazole exposure followed by two weeks of drug-free passages, and MICs for all tested antifungals remained elevated throughout this two-week washout. We fully acknowledge that longer passages would be required to distinguish reversible tolerance from stable resistance.

We agree that the current data do not exclude reversibility over longer timescales, and that extended drug-free passages, reversion assays, or genetic analyses (e.g. sequencing of resistance-associated loci) would be needed to determine whether the phenotype reflects durable resistance or a long-lived, but ultimately reversible, adaptive state. The partial decline in transporter expression after drug withdrawal, alongside sustained MIC elevation, suggests a possible decoupling between acute transcriptional responses and the longer-lived phenotype, and further supports the need for additional work to resolve the underlying mechanism. In response to this comment, we have revised the text to remove use of the term ‘persistent’ which may imply an irreversible, genetically fixed resistance and highlighted the absence of longer-term reversion testing and genetic characterization as limitations and important directions for future studies.

4) Absolute MIC values, variance measures, and statistical tests supporting post-withdrawal persistence for each drug are not reported. Without raw MICs and variability, it is difficult to judge the magnitude and clinical relevance of the observed increases or to verify that post-withdrawal values are significantly different from baseline for all eight drugs (Figure 1C).

Thank you for the feedback. We have revised the presentation of the MIC values extensively to improve clarity and allow for more accurate statistically analysis by opting to reflect changes in MIC as log2 fold change in median versus the Week 0 (untreated) starting MIC. Correlation analysis was subsequently performed in parts - (1) during antifungal treatment – weeks 1-4 (versus week 0) and (2) post treatment weeks 5-6 (versus week 4). We have opted to use non-parametric statistical measures as the discrete, ordinal doubling scale using in antifungal dilutions is parametric rather than linear.

Minor concerns:

1) The PDR10 transcript time-course is presented as a bar plot normalized to ACT1, (Figure 2). Normalization relies on a single housekeeping gene (ACT1) without demonstrated stability under prolonged azole exposure. For example, research in Candida glabrata shows ACT1 is dramatically upregulated (4.5-32.7-fold) by azole treatment, making it unsuitable for qPCR normalization under azole stress (10.1186/1471-2199-13-22) If ACT1 transcription varies with chronic azole treatment, relative normalization could misestimate PDR10 fold changes in Figure 2.

We thank the reviewers for pointing this out. Although it is the default housekeeping gene for Malassezia in gene expression studies, a thorough evaluation on the stability of ACT1 upon on azole exposure has not been performed. To address this, we have performed the gene expression analysis normalized to two additional single copy genes, HH3 and GAPDH. The relative pattern of gene expression of PDR10 remained similar when normalized to GAPDH (See separate reviewer file ‘Reference Gene’). The HH3 primer showed low expression in this strain and may not be suitable as a housekeeping gene. We acknowledge that multiple internal controls may be required to perform accurate normalization of gene expression and hope to do a controlled studies evaluating this comprehensively in the future.

2) The evidence for azole-induced PDR10 upregulation is derived from RT-qPCR fold changes displayed in Figure 2, with clear increases by week 4. Primer efficiency and amplification performance for the PDR10 qPCR assay (standard curves, efficiency values, melt-curve specificity) are not reported.

We have provided the standard curves and above-mentioned details in Supplementary Figure 1.

3) PDR10 is a key mediator of reduced susceptibility to azoles and knockout of the PDR10 gene prevents acquisition of additional resistance. The conclusion that loss of PDR10 prevents acquisition of additional resistance rests on the observed lack of MIC increases in the knockout under clotrimazole and fluconazole and its failure to survive ketoconazole beyond two weeks, as described in the Results text. This interpretation is bolstered by the robust induction of PDR10 during azole exposure (Figure 2) and the clear acquisition of cross-resistance in the wild-type background (Figure 1). These findings are compelling, but the study does not present the construction details or validation of the PDR10 deletion strain. The study does not provide experimental validation of the PDR10 deletion (genotype and loss of transcript/protein) or a genetic complementation rescue to confirm on-target causality. Justify the current findings by linking existing results to necessity under the tested regimen

Construction details along with the experimental validation of the PDR10 deletion strain are demonstrated in an earlier publication (See below). We have amended the text to clarify this and increase visibility of the relevant references.

Leong C, Kit JCW, Lee SM, Lam YI, Goh JPZ, Ianiri G, Dawson TL Jr. Azole resistance mechanisms in pathogenic M. furfur. Antimicrob Agents Chemother. 2021 May 1;65(5):e01975-20. doi: 10.1128/AAC.01975-20. Epub 2021 Feb 22. PMID: 33619053; PMCID: PMC8092866.

Ianiri G, Dagotto G, Sun S, Heitman J. Advancing Functional Genetics Through Agrobacterium-Mediated Insertional Mutagenesis and CRISPR/Cas9 in the Commensal and Pathogenic Yeast Malassezia. Genetics. 2019 Aug;212(4):1163-1179. doi: 10.1534/genetics.119.302329. Epub 2019 Jun 26. PMID: 31243056; PMCID: PMC6707463.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This study addresses an important and timely topic, as the emergence of antifungal resistance poses a significant challenge to clinical management and public health. In this context, the authors investigate the effects of prolonged exposure to widely used azole antifungals (clotrimazole, ketoconazole, and fluconazole) on the development of cross-resistance profiles. Additionally, the study explores the roles of the major facilitator superfamily (MFS) transporters, OPT1 and FLR1, in the emergence of antifungal resistance, providing valuable insights into the underlying mechanisms. However, despite its relevance, the manuscript presents several issues that must be addressed before it can be considered for acceptance:

1) The quality of all figures is inadequate. The current rendering significantly compromises clarity and prevents proper evaluation of the data. High-resolution versions are required.

2) The standard deviation for OPT1_02 in the ketoconazole experiment is excessively high, raising concerns about data reliability. The authors should repeat this analysis and assess the presence of outliers.

3)Line 218: the comparison is unclear and must be specified (e.g., relative to week 0 or another time point). In addition, the expression profile of OPT1_01 at week 3 under fluconazole exposure appears inconsistent and suggests possible overexpression; this result requires careful verification.

4)The absence of statistical analysis for CAF5 expression is a significant omission. The authors must justify this choice or provide the appropriate statistical evaluation.

5) The protein interaction network results are superficially presented and lack meaningful interpretation. This section requires substantial expansion and critical discussion.

6)The Discussion section is insufficiently developed. The comparison with existing literature is limited and fails to adequately contextualize the findings. Additional studies, particularly those addressing the same transporters, should be incorporated. Moreover, the first paragraph and a large portion of the second paragraph are misplaced and would be more appropriate in the Introduction.

Author Response

Reviewer 2:

Comments and Suggestions for Authors

This study addresses an important and timely topic, as the emergence of antifungal resistance poses a significant challenge to clinical management and public health. In this context, the authors investigate the effects of prolonged exposure to widely used azole antifungals (clotrimazole, ketoconazole, and fluconazole) on the development of cross-resistance profiles. Additionally, the study explores the roles of the major facilitator superfamily (MFS) transporters, OPT1 and FLR1, in the emergence of antifungal resistance, providing valuable insights into the underlying mechanisms. However, despite its relevance, the manuscript presents several issues that must be addressed before it can be considered for acceptance:

1)The quality of all figures is inadequate. The current rendering significantly compromises clarity and prevents proper evaluation of the data. High-resolution versions are required.

Original 300dpi TIF files were submitted previously (we are not sure if these are the ones used in the render). We have also adjusted the figures in the word file to higher quality to facilitate reviewers reading.

2) The standard deviation for OPT1_02 in the ketoconazole experiment is excessively high, raising concerns about data reliability. The authors should repeat this analysis and assess the presence of outliers.

We have repeated this experiment to improve the standard deviation for this dataset and revised Figure 2 accordingly.  

3)Line 218: the comparison is unclear and must be specified (e.g., relative to week 0 or another time point). In addition, the expression profile of OPT1_01 at week 3 under fluconazole exposure appears inconsistent and suggests possible overexpression; this result requires careful verification.

Thank you for the observation. We have amended the text and figures to improve clarity. Additionally, we have identified formatting and plotting errors in the expression profile of OPT1_01 which have rectified accordingly.

4)The absence of statistical analysis for CAF5 expression is a significant omission. The authors must justify this choice or provide the appropriate statistical evaluation.

Thank you for this important comment. Statistical analysis for CAF5 expression was in fact performed, but the results were not reported as they were not significant. We apologise for this omission. We have indicated in the figure legend that no statistically significant findings were observed for this dataset.

5) The protein interaction network results are superficially presented and lack meaningful interpretation. This section requires substantial expansion and critical discussion.

We appreciate the reviewer’s critique regarding the protein interaction network. We acknowledge that the depth of our interpretation is currently constrained by the lack of curated, species-specific interactome databases for Malassezia. To address the preliminary nature of these observations, we opted to move the Figure into Supplementary materials and expanded the Discussion to address this theme. While experimental validation remains a future goal for the field, these results provide a high-priority map of the molecular nodes likely driving the resistance shifts observed in our 6-week study

6)The Discussion section is insufficiently developed. The comparison with existing literature is limited and fails to adequately contextualize the findings. Additional studies, particularly those addressing the same transporters, should be incorporated. Moreover, the first paragraph and a large portion of the second paragraph are misplaced and would be more appropriate in the Introduction.

We thank the reviewer for these constructive suggestions, which have significantly improved the clarity and depth of our manuscript. In accordance with this feedback, we have fundamentally restructured the Discussion by relocating the misplaced introductory text to the Introduction, allowing the Discussion to focus immediately on the interpretation of our primary findings. This section has been substantially expanded to include a rigorous comparison with existing literature, particularly regarding the role of ABC and MFS transporters. By incorporating additional studies and contrasting our observations in Malassezia with established efflux-mediated resistance patterns in model fungi like Candida albicans, we have provided the necessary biological context for the identified transporter interactions. We believe this revised synthesis effectively highlights how our findings align with known molecular adaptations to antifungal pressure while specifically identifying unique trends within this emerging pathogen.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript “Efflux-mediated antifungal cross-resistance in Malassezia yeasts” addresses the clinically relevant question of whether specific azoles differ in their propensity to induce cross-resistance in Malassezia and which efflux transporters mediate this process. It presents a sound core idea and interesting observations, particularly the persistent MIC elevation after ketoconazole removal and the differential behavior of OPT1_01 versus OPT1_02. The PDR10Δ experiment provides functional evidence that goes beyond correlative expression data.

In my humble opinion, several points should be corrected or clarified.

Overall, the manuscript requires raw MIC values, justification for dose equivalence, proper statistical treatment of correlations, quantitative presentation of the PDR10Δ results, and a reassessment of the STRING section's contribution. If these issues are addressed, the manuscript could become a solid contribution to understanding azole cross-resistance in Malassezia.

Details

4. Lines 95–97: The authors use 8 µg/ml clotrimazole, 0.12 µg/ml ketoconazole, and 2 µg/ml fluconazole, stating these are sub-MIC concentrations, but the actual Week 0 MIC values for CBS 7982 are not reported. Without knowing what fraction of the MIC each concentration represents, the comparative framework is undermined. If ketoconazole was used at 0.9× MIC while fluconazole was at 0.3× MIC, the stronger cross-resistance response to ketoconazole may simply reflect a higher relative stress dose, not a drug-specific property.
5. Line 100: RNA was extracted only at Weeks 0, 3, 4, and 6. Weeks 1–2 (early adaptive response) and Week 5 (immediate post-removal) are missing, and this omission is not justified. Week 5 is particularly important because the central finding – that ketoconazole-induced MIC elevation persists after drug removal – would be much more convincing if paired with transporter expression data at that time point.
6. Lines 161–168, Fig. 1B–D: Pearson correlation coefficients are computed on n=7 time points. With this sample size, r values below 0.75 are not statistically significant even at α=0.05. The heatmaps show no p-values, no confidence intervals, and no correction for multiple comparisons (8×8 antifungal pairs per treatment group). Statements such as "positive correlation" (line 162) and "limited correlation" (lines 167–168) are unsupported.
7. Lines 170–172: MICs are normalized 0–1 per antifungal, which erases magnitude. A shift from 0.06 to 0.12 µg/ml and a shift from 2 to 128 µg/ml appear identical on the bar graphs. A supplementary table with raw MIC values is essential. Without it, it is impossible to assess whether observed shifts cross clinically meaningful thresholds.
8. Lines 207–215: The knockout experiment is arguably the strongest evidence for PDR10's role, yet it is presented as a single paragraph with no figure, no MIC table, and no expression data for FLR1/OPT1/CAF5 in the knockout background. The statements that "no significant increase in MIC values was observed" (line 209) and that the strain did not survive ketoconazole beyond 2 weeks (line 210) are major findings that require quantitative presentation. Additionally, there is no discussion of whether FLR1 or OPT1 expression is compensatorily upregulated in the PDR10Δ background, which would directly test the hypothesis of partially redundant efflux pathways.
9. Lines 139–142, 228–243: Protein interaction networks are built from S. cerevisiae and S. pombe as proxies, despite sequence homology of only 37–43% (Table 2). At this level of homology, interaction networks may not be conserved. More importantly, the STRING analysis is purely descriptive, generates no testable predictions, and no experimental data derive from it. However, it receives prominent mention in the abstract (lines 27–30), creating an inflated impression of its contribution.

Author Response

Reviewer 3:

Comments and Suggestions for Authors

The manuscript “Efflux-mediated antifungal cross-resistance in Malassezia yeasts” addresses the clinically relevant question of whether specific azoles differ in their propensity to induce cross-resistance in Malassezia and which efflux transporters mediate this process. It presents a sound core idea and interesting observations, particularly the persistent MIC elevation after ketoconazole removal and the differential behavior of OPT1_01 versus OPT1_02. The PDR10Δ experiment provides functional evidence that goes beyond correlative expression data.

In my humble opinion, several points should be corrected or clarified.

Overall, the manuscript requires raw MIC values, justification for dose equivalence, proper statistical treatment of correlations, quantitative presentation of the PDR10Δ results, and a reassessment of the STRING section's contribution. If these issues are addressed, the manuscript could become a solid contribution to understanding azole cross-resistance in Malassezia.

Details

1. Lines 95–97: The authors use 8 µg/ml clotrimazole, 0.12 µg/ml ketoconazole, and 2 µg/ml fluconazole, stating these are sub-MIC concentrations, but the actual Week 0 MIC values for CBS 7982 are not reported. Without knowing what fraction of the MIC each concentration represents, the comparative framework is undermined. If ketoconazole was used at 0.9× MIC while fluconazole was at 0.3× MIC, the stronger cross-resistance response to ketoconazole may simply reflect a higher relative stress dose, not a drug-specific property.

We apologize for not specifying clearly in the manuscript, the rationale behind the choice of treatment concentrations and the error in the use of the term ‘sub-MIC’. To clarify, the MIC values used in this study were two to four steps higher than the known MIC for the specific as previously published (Leong et al 2019, see full reference below). Thus, this should be interpreted as a high stress dose condition. This was based on our observations from previously published studies where we saw an increase in clotrimazole MICs after 4 weeks of successive treatment in a dose dependant manner. The phenomenon was best observed at antifungal concentrations two to four steps higher than the known MIC for the specific strain.  From a clinical perspective, they are representative of the localized 'high-dose' environments found in topical applications or biofilm matrices, where fungi encounter significantly higher drug concentrations than in systemic circulation. We have addressed this in the methods and discussion.

 

Leong C, Kit JCW, Lee SM, Lam YI, Goh JPZ, Ianiri G, Dawson TL Jr. Azole resistance mechanisms in pathogenic M. furfur. Antimicrob Agents Chemother. 2021 May 1;65(5):e01975-20. doi: 10.1128/AAC.01975-20. Epub 2021 Feb 22. PMID: 33619053; PMCID: PMC8092866.

1. Line 100: RNA was extracted only at Weeks 0, 3, 4, and 6. Weeks 1–2 (early adaptive response) and Week 5 (immediate post-removal) are missing, and this omission is not justified. Week 5 is particularly important because the central finding – that ketoconazole-induced MIC elevation persists after drug removal – would be much more convincing if paired with transporter expression data at that time point.

Thank you for this insightful comment. Our study was designed to address two focused questions: (i) whether prolonged azole exposure leads to sustained transporter overexpression, and (ii) whether such changes persist after a defined drug-free period, rather than to map the full kinetics of the early adaptive response. Thus, we selected Weeks 0, 3, and 4 to capture stable expression under chronic exposure, and Week 6 to assess persistence after an extended drug-free interval.

We agree that Week 5 would be valuable for tighter linkage between MIC elevation and transporter expression immediately after drug removal. However, our MIC data already show elevated ketoconazole MIC at Week 5, and the persistence of both increased MIC and transporter upregulation at Week 6 supports a durable rather than transient effect. We have acknowledged the absence of Weeks 1–2 and 5 RNA samples as a limitation in the Discussion.

1. Lines 161–168, Fig. 1B–D: Pearson correlation coefficients are computed on n=7 time points. With this sample size, r values below 0.75 are not statistically significant even at α=0.05. The heatmaps show no p-values, no confidence intervals, and no correction for multiple comparisons (8×8 antifungal pairs per treatment group). Statements such as "positive correlation" (line 162) and "limited correlation" (lines 167–168) are unsupported.

Thank you for the feedback. We have revised the presentation of the MIC values extensively to improve presentation and allow for more accurate statistically analysis. Briefly, we have opted to reflect changes in MIC as log2 fold change in median versus the Week 0 (untreated) starting MIC. Correlation analysis was subsequently performed in parts - (1) during antifungal treatment – weeks 1-4 (versus week 0) and (2) post treatment weeks 5-6 (versus week 4). We have opted to use non-parametric statistical measures as the discrete, ordinal doubling scale using in antifungal dilutions is parametric rather than linear.

1. Lines 170–172: MICs are normalized 0–1 per antifungal, which erases magnitude. A shift from 0.06 to 0.12 µg/ml and a shift from 2 to 128 µg/ml appear identical on the bar graphs. A supplementary table with raw MIC values is essential. Without it, it is impossible to assess whether observed shifts cross clinically meaningful thresholds.

As mentioned above, we revised the presentation of the MIC values extensively to be reflected as a fold change rather than the original MIC values. We hope that this will address the discrepancies in magnitude when compared across different ranges of antifungal dilutions. Additionally, we have provided the MIC table values in Supplementary Table 1. There are no presently no clinical breakpoints currently defined for Malassezia in the CLSI or EUCAST guidelines. As a result, we have used the baseline distribution (Week 0) to define the baseline for a susceptible isolate. This is also based on priori knowledge of CBS 7982 as a susceptible strain in previously antifungal profiling studies

1. Lines 207–215: The knockout experiment is arguably the strongest evidence for PDR10's role, yet it is presented as a single paragraph with no figure, no MIC table, and no expression data for FLR1/OPT1/CAF5 in the knockout background. The statements that "no significant increase in MIC values was observed" (line 209) and that the strain did not survive ketoconazole beyond 2 weeks (line 210) are major findings that require quantitative presentation. Additionally, there is no discussion of whether FLR1 or OPT1 expression is compensatorily upregulated in the PDR10Δ background, which would directly test the hypothesis of partially redundant efflux pathways.

Construction details along with the experimental validation of the PDR10 deletion strain are demonstrated in an earlier publication (See below). We have amended the text to clarify this and increase visibility of the relevant references.

Leong C, Kit JCW, Lee SM, Lam YI, Goh JPZ, Ianiri G, Dawson TL Jr. Azole resistance mechanisms in pathogenic M. furfur. Antimicrob Agents Chemother. 2021 May 1;65(5):e01975-20. doi: 10.1128/AAC.01975-20. Epub 2021 Feb 22. PMID: 33619053; PMCID: PMC8092866.

Ianiri G, Dagotto G, Sun S, Heitman J. Advancing Functional Genetics Through Agrobacterium-Mediated Insertional Mutagenesis and CRISPR/Cas9 in the Commensal and Pathogenic Yeast Malassezia. Genetics. 2019 Aug;212(4):1163-1179. doi: 10.1534/genetics.119.302329. Epub 2019 Jun 26. PMID: 31243056; PMCID: PMC6707463.

We have attempted to evaluate the expression of transporter genes in the PDR10 knockout strain. However, we obtained very low expression readings (compared to CBS 7982) for the respective transporter genes. Cell yield/quality was also low for this strain after azole treatment. It is unclear if the low readings are due to true non-expression, low cell yield or non-detection due to strain level gene variations.

For treatment of the PDR10∆ knockout strain and the associated MICs, we have included the raw values in Supplementary Table 1 and revised Figure 4.  

1. Lines 139–142, 228–243: Protein interaction networks are built from S. cerevisiae and S. pombe as proxies, despite sequence homology of only 37–43% (Table 2). At this level of homology, interaction networks may not be conserved. More importantly, the STRING analysis is purely descriptive, generates no testable predictions, and no experimental data derive from it. However, it receives prominent mention in the abstract (lines 27–30), creating an inflated impression of its contribution.

We thank the reviewer for this comment and agree with the limitations in the interpretations of interaction and STRING networks. We fully agree that interaction networks inferred from S. cerevisiae and S. pombe orthologs must be interpreted with caution due to a lack of comprehensive data/experimental evidence for the Malassezia species.  Our intention was not to imply that individual protein–protein interactions are necessarily conserved at this degree of homology, but rather to use these well-annotated networks as a comparative framework to highlight potential functional groupings and pathways (e.g. transport, stress response, lipid metabolism) that may be relevant to the ketoconazole-induced phenotype in our organism.

To address the preliminary nature of these observations, we opted to move the figure into Supplementary materials and expanded the Discussion to address this theme. While experimental validation remains a future goal for the field, these results provide a high-priority map of the molecular nodes likely driving the resistance shifts observed in our 6-week study.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have substantially revised the manuscript and addressed several earlier concerns, notably by switching to log2 MIC fold-change normalization, adding the PDR10Δ MIC data (Figure 4), and reporting correlation p-values.

In my humble opinion, several points still need clarification or correction. I have phrased each as a possible actionable suggestion.

1. Rhodamine 6G. The assay shows no efflux increase after treatment, and PDR10Δ exceeds wild type (lines 324–327), which is inconsistent with an efflux mechanism. Please move this to the Supplement, state that it was inconclusive (the log-phase limitation is already noted, lines 391–394), and do not present it as mechanistic support. Note also that R6G is mainly an ABC transporter substrate, so it is poorly suited to the MFS transporters (FLR1, OPT1) central to this study.
2. "Two to four steps higher than the MIC" (lines 132–134) and "maximum tolerated dose" (lines 134–136) are two different criteria, and baseline MICs are not given. Please report the Week 0 MIC (µg/mL) and exact fold for each azole, and add a sentence clarifying that the dose was above the MIC but sublethal, allowing a viable subpopulation over four weeks.
3. Over Weeks 1–4, n = 4; the minimum one-tailed p is approximately 0.0417, which is exactly the value of every "significant" result. Please report rho with confidence intervals, label the analysis exploratory, replace "significant" with "consistent positive trend," and state the n = 4 limitation (line 153).

Author Response

Response to Reviewer 3:

1. Rhodamine 6G. The assay shows no efflux increase after treatment, and PDR10Δ exceeds wild type (lines 324–327), which is inconsistent with an efflux mechanism. Please move this to the Supplement, state that it was inconclusive (the log-phase limitation is already noted, lines 391–394), and do not present it as mechanistic support. Note also that R6G is mainly an ABC transporter substrate, so it is poorly suited to the MFS transporters (FLR1, OPT1) central to this study.

We wish to clarify that the rhodamine 6G figure is already a supplemental figure (Supplementary Figure 2) and was not meant to be presented as a key finding in this study.

We thank the reviewer for the additional input and have revised the text to emphasize that some of these findings are exploratory and inconclusive (Lines 275-276). We have also clarified that R6G is mainly an ABC transporter substrate and is therefore not ideal for assessing MFS transporter activity (Lines 315-316). These changes ensure that the R6G data are appropriately contextualized and do not overstate mechanistic conclusions.

As explained in lines 272 to 274, PDR10Δ exceeds that of CBS 7982 because its parent/wild type strain is CBS 14141 which has intrinsically higher rhodamine 6G efflux (vs CBS 7982). However, CBS 14141 is a resistant isolate which is not suitable for sublethal MIC dosing as it is resistant to high antifungal doses. A more direct comparison would be to perform the PDR10 KO in the CBS 7982 strain and evaluate its ability to acquire resistance at the same sublethal doses. We have further explained and outlined these details in the discussion and will consider this for future work.

2. "Two to four steps higher than the MIC" (lines 132–134) and "maximum tolerated dose" (lines 134–136) are two different criteria, and baseline MICs are not given. Please report the Week 0 MIC (µg/mL) and exact fold for each azole, and add a sentence clarifying that the dose was above the MIC but sublethal, allowing a viable subpopulation over four weeks.

Thank you for the feedback. Week 0 MICs are reported in Supplementary Table 1. For clarity have revised Figure 1A to indicate the starting MICs more explicitly. Text in the methods and results has been amended to clarify the sublethal dosing (Lines 185-192).

3. Over Weeks 1–4, n = 4; the minimum one-tailed p is approximately 0.0417, which is exactly the value of every "significant" result. Please report rho with confidence intervals, label the analysis exploratory, replace "significant" with "consistent positive trend," and state the n = 4 limitation (line 153).

We appreciate the reviewer’s careful interpretation of the statistical analysis. As pointed out, due to the very small sample size (n=4) and the borderline one-tailed -values, we are unable to report confidence intervals for Spearman’s rho.  We agree that the results should not be overstated and have therefore revised the manuscript to present the correlation analysis as exploratory, replaced ‘significant’ with ‘consistent positive trend,’ and added a clear statement acknowledging the limitation imposed by the small sample size (Lines 197-198). We have also moved the significance table from Figure 1 to supplementary table 2 and replaced the significance label with labels that denote the observation of a positive trend.

Author Response File: Author Response.pdf