Abstract

Introduction:

Candida parapsilosis is an emerging cause of invasive candidiasis, driven by nosocomial transmission, environmental persistence, and rising azole resistance. Following an increase in detections at our institution, we investigated clinical C. parapsilosis isolates collected in Tyrol, Austria (February–July 2024).

Methods:

Susceptibility to fluconazole and anidulafungin was assessed by CLSI broth microdilution (BMD) and gradient diffusion (Etest) methods. Clonal relatedness was evaluated by whole-genome sequencing (WGS) with SNP-based phylogeny and multilocus sequence typing (MLST); azole-associated genes were analyzed in fluconazole non-susceptible isolates.

Results:

WGS demonstrated a genetically diverse population comprising six clusters, arguing against a single clonal outbreak. The most concerning cluster contained closely related ST11 isolates linked to previously described epidemic ST11 lineages and included both fluconazole-susceptible and non-susceptible isolates; non-susceptibility occurred in only one patient. Non-susceptible isolates carried multiple predominantly heterozygous non-synonymous variants across azole-associated pathways, consistent with an early, genetically heterogeneous stage of resistance development. Marked method-dependent discrepancies were observed for anidulafungin (all susceptible by CLSI BMD, but a substantial fraction non-susceptible by gradient diffusion method), while fluconazole showed higher concordance with minor misclassification.

Discussion:

Combining WGS with standardized susceptibility testing differentiated increased prevalence from clonal transmission, identified a high-risk ST11 lineage with a complex, likely polygenic resistance architecture, and highlighted limitations of gradient diffusion testing for echinocandin categorization. These findings support molecular surveillance and cautious interpretation of gradient diffusion susceptibility testing results to guide infection control and antifungal stewardship.

1 Introduction

The healthcare-associated burden of fungal infections continues to rise, significantly contributing to morbidity and mortality among hospitalized patients worldwide (Bongomin et al., 2017; Rayens and Norris, 2022; Cornely et al., 2025). Candidiasis is a common fungal infection that can manifest in diverse clinical forms and is a major contributor to the global burden of fungal diseases (Lass-Flörl et al., 2024; Cornely et al., 2025). While C. albicans remains the most prevalent species involved in candidiasis, a concerning global shift toward non-albicans Candida (NAC) species has been observed (Lamoth et al., 2018; Pappas et al., 2018). This shift is clinically important because of the potential for antifungal resistance and the associated risk of treatment failure (Whaley et al., 2016; Lass-Flörl et al., 2024).

Among these NAC, C. parapsilosis ranks as the second to third most prevalent species, with its occurrence varying by patient group, clinical setting, and geographic region (Toth et al., 2019). C. parapsilosis possesses several traits that confer a selective advantage for its persistence and prevalence in hospital settings, including high transmissibility and unique colonization patterns (Govrins and Lass-Flörl, 2024). It exhibits a strong capacity to attach and form persistent biofilms on indwelling catheters and implanted medical devices, posing a serious risk to patients undergoing invasive procedures (Toth et al., 2019; Daneshnia et al., 2023; Govrins and Lass-Flörl, 2024). C. parapsilosis infections primarily occur in patients admitted to intensive care units (ICUs) (Pinhati et al., 2016) and neonatal intensive care units (NICUs) (Hernandez-Castro et al., 2010; Qi et al., 2018), and associated outbreaks have been increasingly reported (Magobo et al., 2020; Thomaz et al., 2021). It is also one of the most common causes of candidemia in NICUs, particularly among neonates with risk factors such as low birth weight, indwelling catheters, parenteral nutrition, and mechanical ventilation (Harrington et al., 2017; Govrins and Lass-Flörl, 2024).

Fluconazole has long been the first-line therapy for Candida infections (Pappas et al., 2018). However, its effectiveness has been questioned due to the shift toward NAC species and the rising incidence of azole resistance, especially fluconazole resistance (Whaley et al., 2016; Berkow and Lockhart, 2017). This is primarily driven in C. parapsilosis primarily by ERG11 point mutations affecting the azole target lanosterol 14α-demethylase (Berkow and Lockhart, 2017; Corzo-Leon et al., 2021), as well as by additional resistance mechanisms involving genes such as MRR1, ERG3, UPC2, TAC1, and CDR1B (Branco et al., 2023). Echinocandins are currently considered the first-line empirical therapy for candidiasis (Pappas et al., 2018; Cornely et al., 2025). However, C. parapsilosis naturally exhibits reduced in vitro susceptibility to echinocandins compared to other Candida species because of a P660A substitution in the Fks1 subunit of beta-glucan synthase (Garcia-Effron et al., 2008; Kontoyiannis et al., 2017). In addition, other acquired mutations in hot spot regions 1 and 2 of the FKS gene, such as R658S, L1376F, or F652S have been associated with echinocandin resistance (Siopi et al., 2022; Khalifa et al., 2024). C. parapsilosis, which has historically been linked to clonal outbreaks of drug-susceptible strains, has been associated with adult outbreaks caused by drug-resistant isolates since 2018 (Daneshnia et al., 2023), mostly fluconazole-resistant (Arastehfar et al., 2020; Fekkar et al., 2023). Notably, infections caused by fluconazole-resistant C. parapsilosis ERG11Y132F mutants have been associated with a threefold increase in mortality compared to those with susceptible or non-susceptible strains without the mutation (50% vs. 16%) (Arastehfar et al., 2021). Therefore, the recognition of nosocomial outbreaks and the implementation of effective infection control measures require a rapid and reproducible method for distinguishing closely related isolates.

Following an increase in detections at our institution, we investigated clinical C. parapsilosis isolates collected in Tyrol, Austria (February–July 2024). This single-center study pursued three main objectives; first, to perform genetic analyses using whole-genome sequencing (WGS) to investigate the clonal nature and genetic relatedness of the isolates, second, to characterize the antifungal susceptibility profile of C. parapsilosis to fluconazole and anidulafungin, leading representative agents of the azole and echinocandin classes, using the CLSI broth microdilution (BMD) and the gradient diffusion (Etest) methods, and third, to determine the incidence of azole and echinocandin resistance during the study period and to investigate underlying resistance mechanisms in isolates exhibiting elevated minimum inhibitory concentrations (MICs).

2 Material and methods2.1 Study design, sample collection, and fungal isolates

In this single-center study, all patient specimens that were culture-positive for C. parapsilosis and submitted to the Institute of Hygiene and Medical Microbiology at the Medical University Innsbruck from February 2024 to July 2024 were analyzed. Clinical specimens were received as part of routine diagnostics from different clinical institutions or general practitioners all over Tyrol, Austria, for bacterial and fungal infections and included tissue material, wound swabs, urine, puncture fluid, bronchoalveolar lavage fluid, feces, nail material, and one central venous catheter tip. No specific inclusion or exclusion criteria were applied to the study. Multiple isolates from the same patient were included to monitor changes in the antifungal susceptibility pattern and detect potential resistance-associated mutations.

After receipt of specimens, samples were analyzed according to local microbiological standards; cultural growth of C. parapsilosis was identified and confirmed by Matrix-assisted Laser Desorption Ionization Time of Flight (MALDI-TOF) mass spectrometry, as described by Steixner et al. (2025b), using a MALDI Biotyper smart (Bruker Daltonics, Bremen, Germany). As recommended by the manufacturer, a score of ≥2 was considered sufficient for species identification. After identification, the strains were stored at −20 °C and re-cultivated on Sabouraud’s dextrose agar (SDA) plates (BioMerieux, Vienna, Austria) at 37 °C for 24 h prior to the experiments.

2.2 Antifungal susceptibility testing and interpretation

Susceptibility testing for fluconazole and anidulafungin was performed by measuring the MIC according to yeast broth microdilution by CLSI BMD (CLSI, 2017), using fluconazole powder (Merck, Darmstadt, Germany) and anidulafungin powder (Merck, Darmstadt, Germany), and gradient diffusion method. C. parapsilosis ATCC 22019 and C. krusei ATCC 6258 were used as reference strains for all antifungal susceptibility tests. Gradient diffusion testing was performed on ready-to-use Roswell Park Memorial Institute (RPMI) 1640 agar plates (Axon-Lab, Tyrol, Austria) using fluconazole (BioMerieux, Vienna, Austria; 0.016 mg/L–256 mg/L) and anidulafungin (BioMerieux, Vienna, Austria; 0.002 mg/L–32 mg/L) Etest strips, as described previously (Vahedi-Shahandashti et al., 2025).

MIC values were obtained after 24 h of incubation by both methods and interpreted according to CLSI BMD breakpoints (CLSI, 2020) for both fluconazole and anidulafungin, categorizing isolates as susceptible (MIC ≤2 mg/L), intermediate (MIC of 4 mg/L), or resistant (MIC ≥8 mg/L). The MIC for both antifungals using the gradient diffusion method was determined at 80% inhibition, excluding any trailing observed, as described by the manufacturer. MICs were determined at 50% inhibition, as defined by CLSI (CLSI, 2017). For both antifungals, MIC50 and MIC90 values, defined as the MIC inhibiting the growth of 50% and 90% of isolates, respectively, were calculated.

2.3 DNA extraction

Genomic DNA of all isolates and the control strain C. parapsilosis ATCC 22019 (designated as CR6 in the present study) was extracted from 24 h-old SDA cultures using a cetyltrimethylammonium bromide (CTAB)-based method, adapted for fungal isolates with minor modifications, as described previously (Najafzadeh et al., 2010; Sun et al., 2010).

Briefly, fungal material was disrupted in TE (400 mM Tris, 10 mM Na-EDTA, pH 8.5–9) buffer with glass beads (G9143: 212–300 µm, Merck, Darmstadt, Germany), followed by 10% sodium dodecyl sulfate (Carl Roth, Karlsruhe, Germany) and proteinase K (Carl Roth, Karlsruhe, Germany) treatment, CTAB/NaCl precipitation, SEVAG (chloroform:isoamyl alcohol, 24:1) extraction, and isopropanol precipitation. Deviating from the aforementioned protocols, the samples were incubated at 65 °C instead of 55 °C, and the vortexing steps were extended to 5 min and 10 min. DNA was re-suspended in nuclease-free, DEPC-treated water (Carl Roth, Karlsruhe, Germany), and an RNase A digestion step with 2 µL RNase A (Qiagen, Hilden, Germany; 100 mg/mL) for 30 min at 37 °C, followed by 2 min at 65 °C, was performed prior to downstream applications.

2.4 Whole genome sequencing and sequence data analysis

Paired-end 2 × 150 bp whole-genome sequencing was performed at Eurofins Genomics (Eurofins, Constance, Germany) using an Illumina NovaSeq X+ (Illumina, San Diego CA, USA).

To detect single-nucleotide polymorphisms (SNPs) and structural variants, our WGS data, as well as data from three confirmed outbreaks, including outbreaks from Berlin (Brassington et al., 2025), Bloemfontein, and Johannesburg (Bergin et al., 2024), were analyzed using the perSVade pipeline v1.02.6 (Schikora-Tamarit and Gabaldon, 2022) with default settings and the recommended adjustments for diploid organisms. C. parapsilosis CDC317 was used as a reference. A comparison with known outbreak datasets was used as a reference to investigate the genetic relatedness of our isolates, allowing us to determine whether the isolates in our dataset showed levels of genetic similarity comparable to those observed in described clonal outbreaks. InterProScan v5.73-104.0 (Jones et al., 2014; Blum et al., 2025) was used to annotate the functional domains of genes carrying mutations.

A custom Python v3.11.8 (van Rossum, 2010) script was used to calculate the maximum pairwise symmetric SNP differences within the three known outbreaks (Bergin et al., 2024; Brassington et al., 2025) to assess the genetic relatedness of C. parapsilosis isolates and establish a suitable cluster cutoff for our dataset (https://github.com/DavidEiseleIMED/SymmetricSNP/tree/main). The libraries used within the Python script included the standard libraries re v2.2.1, csv v1.0, os, sys, and collections, and external libraries such as pandas v2.2.2 (McKinney, 2010; The pandas development team, 2024) and numpy v1.26.4 (Harris et al., 2020). From the homozygous SNPs that were called by all three SNP-callers employed by perSVade, such as bcftools (Li, 2011; Danecek et al., 2021), freebayes (Garrison and Marth, 2012), and HaplotypeCaller (Poplin et al., 2017; van der Auwera and O’Connor, 2020), a merged vcf file was constructed with bcftools merge v1.21. The script vcf2phylip (Edgardo M. Ortiz, 2018) was used to construct pseudogenomes, which were then analyzed using IQ-tree (Nguyen et al., 2015). Sequences with unusual GC content were analyzed using FastQ Screen to test for contamination (Wingett and Andrews, 2018). iTOL v7.4 was used for the visualization and annotation of the phylogenetic tree (Letunic and Bork, 2024). The phylogenetic tree was rooted using the “root on midpoint” option. In total, five pseudogenomes from our samples failed IQ-tree’s composition χ2-test, including CP18, CP21, CP22, CP43, and CP61. To identify genetic clusters, the maximum symmetric SNP difference was calculated for each of the three outbreaks investigated. The highest symmetric SNP difference of 731 SNPs was found in the Berlin outbreak and was used in the present study as a threshold for being considered at the same level of genetic similarity. The symmetric SNP difference within the Johannesburg outbreak was the lowest at 310, and the Bloemfontein outbreak with 557 was intermediate.

2.5 Multilocus sequence typing

The sequences of our isolates, as well as those of the three known outbreaks from Berlin (Brassington et al., 2025), Bloemfontein, and Johannesburg (Bergin et al., 2024), were analyzed using a multilocus sequence typing (MLST) annotation tool developed by Seemann (2018) v2.23.0 to which the MLST scheme by Brassington et al. (2025) was added. To avoid IUPAC ambiguities, as recommended by Seemann’s MLST annotation tool (Seemann, 2018), the alternative allele was used to determine the sequence type (ST) when a heterozygous mutation was present. STs for both the reference and alternative alleles are provided in the Supplementary Material S1. Novel sequence types not covered by the original MLST scheme were added to the scheme shown in Supplementary Material S2.

2.6 Statistical analysis

All statistical analyses were performed using GraphPad PRISM v10.2.3. To evaluate the changes in the number of C. parapsilosis isolates from February to July in 2022, 2023, 2024, and 2025, monthly data were analyzed at the isolate, patient, and isolate-per-patient (IPP) levels. IPP ratios were calculated by dividing the total number of isolates by the total number of patients. Data normality was assessed using the Shapiro–Wilk test, and homogeneity of variance across groups was evaluated using the Brown–Forsythe test. Differences between years were analyzed using one-way analysis of variance (ANOVA), followed by the Holm–Šídák multiple-comparison test. Susceptibility testing results were compared using the Wilcoxon matched-pair signed-rank test. Statistical significance was set at p <0.05.

3 Results3.1 Increasing trend of C. parapsilosis isolation compared to previous years

Between February and July 2024, C. parapsilosis was cultured from 47 clinical specimens collected from 24 patients at our institution, resulting in an IPP ratio of 1.96. The isolates originated from various body sites (Supplementary Table 1). Multiple isolates were obtained from some patients as part of routine clinical care, either from different anatomical sites or through repeated sampling over time. The anatomical sites and dates of collection for each isolate are provided in Supplementary Table 1. During the corresponding period, the IPP ratio increased from 1.22 in 2022 (22 isolates from 18 patients) and 1.48 in 2023 (43 isolates from 29 patients) to 1.96 in 2024 (47 isolates from 24 patients), indicating an upward trend in IPP. Recurrent isolates from the same patient were retained for further analysis to assess potential changes in antifungal susceptibility patterns and investigate the occurrence of resistance-associated mutations over time.

3.2 Method-dependent differences in antifungal susceptibility testing profile

Fluconazole gradient diffusion testing resulted in a median MIC of 1 mg/L (range: 0.125 mg/L–≥32 mg/L), whereas CLSI BMD yielded a median MIC of 0.5 mg/L (range: 0.125 mg/L–4 mg/L). Two isolates (obtained from one patient) were classified as resistant by the gradient diffusion method but were considered intermediate by CLSI BMD. For anidulafungin, the median MICs were 2 mg/L using the gradient diffusion method and 1 mg/L using CLSI BMD. MIC ranges differed considerably between the methods: 0.004 mg/L–16 mg/L for Etest versus 0.016 mg/L–2 mg/L for CLSI BMD. Gradient diffusion testing classified three isolates (obtained from two patients) as resistant and eight isolates (obtained from two patients) as intermediate, whereas CLSI BMD categorized all isolates as susceptible.

Overall, fluconazole antifungal susceptibility testing (AFST) by both methods classified 94.1% of isolates as susceptible (Figure 1A). Gradient diffusion and CLSI BMD classified 2.1% and 6.4% of strains as intermediate, respectively, while only gradient diffusion testing classified 4.3% of isolates as resistant, whereas CLSI BMD did not classify any strain as resistant. For anidulafungin (Figure 1B), all isolates were classified as susceptible by CLSI BMD, whereas gradient diffusion testing classified 76.6% of the isolates as susceptible, 17.0% as intermediate, and 6.4% as resistant. As repeated isolations from the same patient were analyzed, the MIC distributions should be interpreted as isolate-level frequencies and not as patient-level prevalence.

Minimum inhibitory concentration (MIC) distribution of (A) fluconazole and (B) anidulafungin from Candida parapsilosis isolates (n = 47) using gradient diffusion (Etest) and broth microdilution (CLSI) methods. Multiple isolates from the same patient are included; therefore, MIC distributions reflect isolate-level rather than patient-level frequency. Clinical and Laboratory Standards Institute (CLSI) breakpoints (CLSI, 2020) are visually indicated by background shading and lines: green for susceptible (MIC ≤2 mg/L), gray for intermediate (MIC = 4 mg/L), and red for resistant (MIC ≥8 mg/L) categories. Gradient diffusion obtained MICs were rounded to the next higher log2 dilution for comparison reasons.

Statistical analysis revealed a significant method-dependent difference in the categorization of both antifungals tested (p <0.0001). Essential agreement, defined as MIC values within ± one two-fold dilution step between gradient diffusion and CLSI BMD, was observed in 55.3% of cases for fluconazole and 74.5% for anidulafungin. Discrepancies were further classified into very major errors (VME, gradient diffusion-susceptible/CLSI BMD-resistant), major errors (ME, gradient diffusion-resistant/CLSI BMD-susceptible), and minor errors (MiE, one intermediate method, the other susceptible or resistant). No VMEs were observed for either antifungal agent. However, three (6.4%) ME were detected for anidulafungin. MiEs were notably more frequent for anidulafungin (17.0%) than for fluconazole (4.3%), indicating a greater categorical discrepancy between the two testing methods for anidulafungin.

3.3 Cluster investigation and MLST classification

MLST and SNP analyses showed no clonal outbreak but a diverse C. parapsilosis population. Our isolates belonged to nine STs (ST01, two isolates obtained from two patients; ST09, 2/2; ST10, 5/5; ST11, 5/3; ST22, 20/4; ST44, 1/1; ST63, 10/7; ST66, 1/1; and ST67, 1/1), six SNP clusters, including one pseudocluster (cluster 4), and 10 singletons (Table 1, Figure 2).

ClusterMinimum SNP differenceMaximum SNP differenceSequence typeCluster 165499ST11Cluster 2524539ST10Cluster 335261ST22Cluster 4*5151ST22Cluster 5185362ST63Cluster 661448ST63Bloemfontein124537ST11Johannesburg140310ST53Berlin28731ST01, ST08, ST68

Internal symmetric SNP difference.

*Cluster 4 can be regarded as a pseudocluster.

SNP, Single nucleotide polymorphism.

Phylogram based on homozygous single-nucleotide polymorphisms (SNPs) of Candida parapsilosis isolates from this study (n = 47) and a reference strain (C. parapsilosis ATCC 22019; CR6) together with isolates from three previously described outbreaks: Berlin (n = 38) (Brassington et al., 2025), Bloemfontein (n = 14), and Johannesburg (n = 12) (Bergin et al., 2024). Phylogeny is shown at (A) isolate-level and (B) patient-level. The phylogeny created by IQ-tree, which was based on the pseudogenomes of around 11,000 homozygous variable positions, was visualized using iTOL. A tree distance of 0.1 corresponds to around 1,000 homozygous SNP differences. The isolates from the three different outbreaks were condensed to the most related five isolates for each of the outbreaks. As a threshold, 731 SNPs, based on the already described, uncondensed outbreaks (Bergin et al., 2024; Brassington et al., 2025) of C. parapsilosis, were used to determine clusters. Sequence types (ST) were determined by multilocus sequence typing (MLST) analysis (Seemann, 2018; Brassington et al., 2025), and are highlighted in colored dots. The different clusters are shown by the colored label backgrounds. Susceptibility profile of fluconazole and anidulafungin against isolates according to gradient diffusion method (using Etest strips) and broth microdilution (Clinical Laboratory Standards Institute (CLSI)) are shown in beige (susceptible), light-blue (intermediate) and dark-blue (resistant) according to CLSI breakpoints (CLSI, 2020). All antifungal susceptibility testing (AFST) results were acquired using CLSI except the results from the Berlin outbreak which were performed according to EUCAST. In (B), entries marked with an asterisk (*) represent multiple isolates condensed for visualization; no isolates present in (A) were removed from the analysis. Further, the AFST with the highest minimum inhibitory concentration (MIC) results are shown for these entries. AND, anidulafungin; FLU, fluconazole; BMD, broth microdilution; ET, Etest gradient diffusion; ND, not determined.

Symmetric SNP differences of clusters, outbreaks, and singletons were similar but above the defined cluster threshold (Table 2).

ClusterMinimum SNP differenceMaximum SNP differenceCluster 1Bloemfontein8991,283Cluster 3Cluster 4*808839Cluster 5Cluster 6762852CP4Berlin557744CP62Berlin853999CP4CP62758758CP3Cluster 21,1951,242CP30Cluster 21,1111,160

Symmetric SNP differences between clusters, outbreaks and singletons.

*Cluster 4 can be regarded as a pseudocluster.

SNP, Single nucleotide polymorphism.

Cluster 1 isolates derived from three different patients, with patient 1 with three isolates (CP1, CP19, CP20) and patients 13 and 27 with isolates CP22, and CP63, respectively, which differs from the ST11 Bloemfontein outbreak isolates by a minimum of 899 SNPs (Table 2). ST10 was present in three isolates/patients of Cluster 2 (patients 11, 19, and 23; CP17, CP44, and CP54, respectively) and the singletons CP3 (patient 3) and CP30 (patient 12). ST09 was found in two singletons/patients (patients 18 and 25; CP43 and CP61, respectively) and the control strain (CR6). All isolates/patients from Clusters 3 and 4 belonged to ST22. The newly described ST63 included three isolates/patients from Cluster 5 (patients 16, 24, and 28; CP33, CP55, and CP64, respectively) and seven isolates/four patients from Cluster 6 (patients 4, 6, 7, and 9; CP6, CP9-11, CP12 and CP27, and CP14, respectively). The singleton/patient CP4 (patient 2) was genetically related to the Berlin outbreak. No patient with isolates from different clusters was found. Patient 12 had isolates belonging to three different STs, namely ST22 (CP21) from cluster 3, and two singletons, ST66 (CP18) and ST10 (CP30).

Some isolates from cluster 1, all from the same patient, showed decreased susceptibility or resistance: isolates CP1 and CP19 showed resistance against fluconazole by the gradient diffusion method, were classified as intermediate by CLSI BMD, and isolate CP20 was categorized as fluconazole-intermediate by both methods. Nine isolates from patient 15 in Cluster 3 showed alterations in AFST by gradient diffusion testing for anidulafungin: two isolates (CP36 and CP38) showed anidulafungin resistance, while seven isolates (CP39, CP42, CP45, CP46, CP47, CP52, and CP53) were classified as intermediate to anidulafungin.

3.4 Azole-resistance-associated mutations identified in non-susceptible isolates from a single patient

The three fluconazole non-susceptible isolates (CP1, CP19, and CP20; Cluster 1), all originating from the same patient and categorized as intermediate by CLSI BMD and intermediate/resistant by gradient diffusion testing, were analyzed for exclusive SNPs shared between them and absent in all susceptible isolates to identify SNPs potentially responsible for their decreased susceptibility (intermediate to fluconazole). A total of 39 genes with nonsynonymous SNPs were discovered (Table 3), of which 26 were SNPs within functional domains recognized by InterProScan. Of these mutations, three were homozygous and the other 23 were heterozygous. Of the remaining 13 mutations detected outside the InterPro domains, three were homozygous and 10 were heterozygous. CP1, CP19, and CP20 also did not carry the ERG11Y132F mutation present in the reference genome or the MRR1A854V mutation detected in the Bloemfontein outbreak (Bergin et al., 2024); instead, they showed the same genotype as susceptible strains.

Gene IDProtein name (similar to)#Protein function of similar protein###MutationInside InterPro domain XM_036806891.1XP_036662873.1 (FLO8##)Cell–cell-adhesion, flocculation, invasive growthS260L XM_036807146.1XP_036662896.1 (SEC27)ER-to-Golgi vesicle-mediated transport, localization of intracellular mRNAsA778V XM_036807519.1XP_036664348.1 (DFI1)Calcium-mediated signaling, cell adhesion, invasive filamentous growth, MAPK cascadeN347D XM_036807888.1XP_036664680.1 (RRD1)Autophagy, stress-response, DNA repair, G1/S transition of mitotic cycle, mitotic spindle organizationL46Q XM_036807900.1XP_036664691.1 (HDA1)Filamentous growth, cellular response to pH and starvation, histone deacetylaseE654* XM_036807998.1XP_036664778.1 (MTC5)TORC1 regulation, protein transport, cellular response to amino acid starvationA200P XM_036808069.1XP_036664842.1 (ATP25)Stability of ATP9 mRNA, assembly of F1F0 ATP synthase complexE272K XM_036808075.1XP_036664848.1–G112R XM_036808173.1XP_036664936.1 (FTH2)Iron ion transmembrane transport, prostaglandin metabolic processE173K XM_036808310.1XP_036665060.1 (DJP1)Peroxisome organizationR27* XM_036808538.1XP_036665265.1 (Hyphally regulated cell wall protein N-terminal family protein–P132R XM_036809930.1XP_036666518.1 (SHQ1)Unfolded protein binding, box H/ACA snoRNP assemblyM271V XM_036810024.1XP_036666602.1 (TPP1/PNK1)DNA repairV106I XM_036810053.1XP_036666629.1 (TBF1)Negative regulation of chromatin silencing and telomere maintenanceS396N XM_036810192.1XP_036666754.1 (ABZ2)Carboxylic acid biosynthesisH81R XM_036810736.1XP_036667243.1 (FOX2)Fatty acid beta-oxidationK321E XM_036811024.1XP_036667502.1 (ARH1)Ubiquinone biosynthesis, iron homeostasisR9* XM_036811176.1XP_036667640.1 (RCH1)Cytosolic calcium homeostasisP216T XM_036811287.1XP_036667740.1 (TRM3)tRNA methylaseH59Y XM_036811323.1XP_036662822.1 (FIG4)Phosphatidylinositol phosphate catabolismG496S XM_036811675.1XP_036668088.1 (JEN1)Transport of selenite, pyruvate and lactateA232E XM_036811700.1XP_036668111.1 (BLM3)DNA repairT856I XM_036811751.1XP_036668157.1–L355S XM_036812158.1XP_036668523.1 (ENP2)18S rRNA and ribosomal subunit biogenesisM35I XM_036812236.1XP_036663364.1–A278V XM_036812375.1XP_036663489.1(HEM13)Heme biosynthesisH127ROutside InterPro domain XM_036806997.1XP_036663877.1–S374N XM_036807790.1