Comparative gene expression analysis in closely related dermatophytes reveals secondary metabolism as a candidate driver of virulence

| Genetics and Molecular Biology | Research Article Comparative gene expression analysis in closely related dermatophytes reveals secondary metabolism as a candidate driver of virulence Lenka Machová,1,2 Martin Kostovčík,1 Karel Švec,1 Vít Hubka,1 Miroslav Kolařík,1 Adéla Wennrich1 AUTHOR AFFILIATIONS See affiliation list on p. 16. ABSTRACT Dermatophytes are important fungal skin pathogens affecting humans and animals worldwide. Although several virulence factors have been identified using genomic, proteomic, and transcriptomic approaches, their roles remain incompletely understood. In this study, we applied a comparative approach using four closely related taxa within the Trichophyton benhamiae complex, which differ in infectivity despite sharing common hosts. We focused on the emerging zoonotic pathogen T.benhamiae var. luteum, currently responsible for epidemic outbreaks in Europe, and compared it to its less infective relatives. A set of 16 candidate genes, informed by preliminary transcriptomic screening, was assessed via RT-qPCR across 12 strains grown in vitro (Sabouraud dextrose broth) and ex vivo (murine skin explants). Genes associated with secondary metabolism were consistently upregulated under ex vivo conditions, particularly in T.benhamiae var. luteum. While two of the biosynthetic gene clusters examined are linked to known metabolites, others remain uncharacterized. These findings reveal key gene expression differences that may explain the enhanced infectivity of emerging strains and underscore the potential role of secondary metabo lites in dermatophyte virulence. They also highlight the need for improved genome annotation in T.benhamiae to better understand the molecular basis of pathogenesis. IMPORTANCE Trichophyton benhamiae var. luteum is an emerging fungal pathogen responsible for a rising number of skin infections transmitted from guinea pigs to humans, especially in Europe. We investigated why this pathogen spreads more effectively than its close relatives, which infect the same hosts but are less epidemic. Using a laboratory model that mimics skin infection, we found that genes involved in producing fungal compounds—called secondary metabolites, some of which act as toxins—are more active in this pathogen. These compounds may help the fungus suppress the host immune response and establish infection. Our findings shed light on how fungal pathogens adapt to their hosts and highlight gene pathways that could be targeted in future diagnostics or treatments. Understanding these mechanisms is key to managing emerging fungal threats in both animals and humans. KEYWORDS ex vivo skin model, RT-qPCR, virulence factors, gene expression, secondary metabolites, Trichophyton benhamiae complex, dermatophytes Dermatophytes are widespread fungal pathogens with a prevalence of 20%–25% in the world’s human population (1). In recent decades, awareness of these fungi has grown due to the rise in antifungal-resistant strains and the discovery of new species (2–4). One major public health issue is the rapid spread of Trichophyton benhamiae var. luteum, commonly referred to as “yellow-phenotype strains” of T.benhamiae in Europe. This emerging zoonotic pathogen has spread extensively in European guinea pig farms and pet shops, and among their owners and their children, especially young individuals November 2025 Volume 13 Issue 11 10.1128/spectrum.01383-25 1 Editor Patricia Albuquerque, Universidade de Brasilia, Brasilia, Federal District, Brazil Address correspondence to Adéla Wennrich, [email protected]. The authors declare no conflict of interest. See the funding table on p. 16. Received 3 May 2025 Accepted 2 September 2025 Published 9 October 2025 Copyright © 2025 Machová et al. This is an openaccess article distributed under the terms of the Creative Commons Attribution 4.0 International license. (4–6). Interestingly, closely related species, T.japonicum and T.europaeum (commonly referred to as “white-phenotype strains”), have been present on the same hosts in Europe for decades without triggering similar outbreaks and epidemics (4). Coinfection of guinea pigs with both T.benhamiae var. luteum and “white-phenotype strains” (i.e., T.europaeum and T.japonicum) has been documented; however, the epidemic strains of T.benhamiae var. luteum cause human infections up to 30 times more frequently and up to four times more frequently in guinea pigs than its closely related species (4, 6, 7). One explanation for the lower incidence of T.japonicum and T.europaeum compared with T. benhamiae var. luteum in guinea pig and human populations may be their lower infectivity. In addition, a population of a different variety (T.benhamiae var. benhamiae) belonging to the same species as the epidemic strains (T.benhamiae var. luteum) occurs in North America, yet no reports of epidemics caused by these strains have been documented in that area (4). It remains unclear why the epidemic strain spreads so effectively among European guinea pigs and children compared with its close relatives or even other populations of the same species. Dermatophytes have developed various mechanisms for spreading within hosts and invading their tissues. A key feature is their sophisticated machinery for degrading keratin and other skin components, primarily through the secretion of proteases. Notable examples include subtilisin-like proteases, dipeptidyl peptidases, leucine aminopepti dases, and metallopeptidases (8, 9). Hosts counteract dermatophyte infections using several defense mechanisms, such as high body temperature, and the mildly acidic pH of the skin, as well as innate immunity like keratinocytes, macrophages, neutrophils, and adaptive immunity, especially the Th1 and Th17 cell-mediated response (10, 11). Dermatophytes can evade some host barriers and produce a wide range of proteases that vary according to the environmental pH (12–15). As a result of keratin degrada tion by dermatophytes, the pH of the surrounding area increases, which impairs the host’s natural immunity (14, 16). Compounds produced by dermatophytes, such as penicillin G, melanin, xanthomegnin, and vioxanthin are known or suspected toxins and/or immunomodulators (17–20). Nevertheless, the role of secondary metabolites in dermatophyte pathogenesis remains underexplored. Despite the high genetic similarity among dermatophytes (4, 21), different species exhibit distinct host preferences and varying levels of virulence across host species. These differences likely arise from variations in gene expression and enzyme activity regulation (22). Several approaches can be used to identify potential virulence factors. In dermatophytes, comparative studies have primarily focused on gene expression during in vitro saprobic and in vivo parasitic growth phases (23, 24). Various alternatives to the in vivo approach have been introduced as useful, more ethical, cost-effective, and reproducible options (25). Several ex vivo skin models have been developed in addition to the traditional in vitro models to better replicate real conditions of infec tion in dermatophytes (26–31). These include reconstructed human epidermis (30) and organoid-based models (31), which offer human tissue relevance but may lack full skin architecture, immune elements, or mechanical barrier integrity. In contrast, animalderived explants (26–29) preserve native skin structure and allow short-term fungal colonization under near-physiological conditions. Another option is the comparison of related species differing in infectivity or virulence. However, this requires the use of very closely related populations or species to ensure that the observed results are not simply attributed to differences between species during their speciation. This compara tive approach has not been used for dermatophytes. In this study, we applied a comparative gene expression approach to identify candidate virulence-associated genes in dermatophytes. We compared more infective taxa with less infective, yet closely related ones to identify differences in gene expression potentially linked to virulence. This approach allows us to distinguish general virulence factors shared across taxa from those potentially associated with enhanced infectivity. Candidate genes were selected based on preliminary transcriptomic data and literature Research Article Microbiology Spectrum November 2025 Volume 13 Issue 11 10.1128/spectrum.01383-25 2 evidence, and their expression was then validated across a broader strain data set using RT-qPCR to strengthen the robustness of observed patterns. MATERIALS AND METHODS Strains and cultivation conditions A total of 12 strains—three from each of the four closely related taxa, T.benhamiae var. benhamiae, T.benhamiae var. luteum, T.japonicum, and T.europaeum—were used (Table 1). All strains are publicly available from the Culture Collection of Fungi, Charles University (CCF). Species identification was confirmed using ITS rDNA sequencing (4). Cultures were maintained on malt extract agar (MEA; HiMedia) at 6 °C in the dark. Prior to the experiment, all strains were subcultured to ensure purity. To minimize the risk of contamination, a single-colony isolation step was performed by spreading diluted inoculum on MEA plates and selecting a single morphologically typical colony for further use. Strains were also briefly cultured in liquid medium supplemented with chloramphe nicol, and bacterial contamination was ruled out by microscopic inspection. To prepare the inoculum, strains were grown in Sabouraud dextrose broth (SDB; HiMedia) at 30 °C for 8 days, shaking at 200 rpm (Digital Orbital Shaker, Heathrow Scientific). The resulting biomass was homogenized by vortexing and diluted to obtain approximately equal cell densities. This method was used instead of standard spore suspensions because some strains—particularly T.benhamiae var. luteum—were sterile. Three to five biological replicates were prepared for each strain and for both cultivation conditions: liquid culture and ex vivo skin model. Liquid cultures were initiated by inoculating 30 mL of SDB with 4 µL of homogenate and incubated under the same conditions described above. Murine skin models The ex vivo murine skin explant (MSE) model protocol was developed and optimized based on published methodologies (31–33). Healthy three-month-old male BALB/c mice were obtained from the breeding facility of the Institute of Microbiology, CAS. All procedures involving animals were conducted in full compliance with Czech legislation (Act No. 246/1992 Coll., on the protection of animals against cruelty) and the Guidelines for the Care and Use of Laboratory Animals. TABLE 1 Strains used in the gene expression analysis Taxon Referred to as Strain IDaCCF identifier Year of isolation Source T.benhamiae var. benhamiae a IHEM 4710 TCCF6484 1956 Human skin b USA 3356 CCF6486 2010 Dog c IHEM 3287 CCF6483 1970 Monoascosporic isolate no. 3 from crossing of strains IHEM 24908× IHEM 4710 T.benhamiae var. luteum a IHEM 25742 CCF6474 2012 Human skin b SK 1248/12 CCF4852 2012 Skin near mouth of a 6-year-old girl c IHEM 25077 CCF6475 2011 Human scalp and neck skin T.europaeum a IHEM 20161 = CBS 112371 CCF6479 2002 Face of human (after contact with guinea pigs) b IHEM 25062 CCF6477 2011 Face of human c ME 192/12 CCF6380 2012 Thigh skin of a 16-year-old girl T.japonicum a IHEM 17701TCCF6481 1963 Human skin b VUT 97010 CCF6489 1997 Rabbit c NUBS 13002 CCF6488 Unknown Human skin aSuperscript “T” denotes ex-type strains. Abbreviation of the culture collections: IHEM-BCCM/IHEM - Biomedical Fungi and Yeast Collection, Belgium; USA-University of Illinois at Urbana-Champaign, USA; SK-Clinic of Dermatovenerology, General University Hospital in Prague, Czech Republic; ME-Hospital in Pardubice, Czech Republic; VUT-Nihon University School of Veterinary Medicine, Japan; NUBS-Nihon University School of Veterinary Medicine, Japan. Research Article Microbiology Spectrum November 2025 Volume 13 Issue 11 10.1128/spectrum.01383-25 3 The dorsal skin of live mice was shaved using an electric clipper (Aesculap Exacta GT 416; B. Braun, Germany), and the animals were subsequently euthanized. The shaved area was swabbed with 70% ethanol. Skin sections (2 × 2 cm) were excised under sterile conditions, cleaned of adipose tissue, and placed into 0.1% benzalkonium bromide solution for disinfection. Within 30 minutes, samples were washed with ice-cold phosphate-buffered saline (PBS; Sigma-Aldrich), immersed in 1% Penicillin-Streptomycin (Pen-Strep; Sigma-Aldrich) for 10 minutes, then washed twice again with PBS and Pen-Strep. Finally, skin samples were placed on sterile gauze in 6-well plates. Each sample received 1 mL of murine skin fluid (MSF), composed of 10% (v/v) heat-inactivated fetal bovine serum (Gibco), 8% (v/v) MSF buffer, and 1% (v/v) Pen-Strep. MSF buffer (adjusted to pH 6.4) contained: 8.0 g/l NaCl, 0.4 g/l KCl, 0.0875 g/l Na₂HPO₄, 0.0625 g/l KH₂PO₄, 0.2 g/l MgSO₄, and 6.25 g/l D-glucose. Four microliters of SDB culture supernatant, microscopically confirmed to be free of bacterial contamination, was added to each skin sample. The samples were incubated at 30 °C and 5% CO₂ for 8 days without shaking (MCO-170AICUV-PE incubator, Panasonic). MSF was refreshed daily. The 8-day cultivation period was chosen based on a pilot RNAseq analysis. RNA extraction and processing Mycelia from liquid cultures were harvested by centrifugation at 1000 rpm for 10 minutes. Mycelium or colonized skin was ground in liquid nitrogen, resuspended in 1 mL of TRIzol reagent (Thermo Fisher Scientific), and stored at –35 °C until RNA isolation. RNA was extracted following the manufacturer’s instructions (34). Equal amounts of total RNA were used for downstream cDNA synthesis, but fungal biomass was not quantified separately from host tissue in ex vivo samples. RNA concentration and purity were assessed using a NanoDrop 1000 spectrophotom eter (Thermo Fisher Scientific). Genomic DNA was removed with the TURBO DNA-free Kit and protected using RNaseOUT Recombinant Ribonuclease Inhibitor (both Thermo Fisher Scientific). RNA integrity was confirmed by 1% agarose gel electrophoresis stained with ethidium bromide (HiMedia). Concentration was measured using Qubit RNA HS and BR Assay Kits on a Qubit 2.0 Fluorometer (Thermo Fisher Scientific). Candidate gene identification by RNA sequencing To identify genes potentially involved in virulence and host interaction, RNA-seq was performed on two representative strains—T.benhamiae var. luteum (IHEM 25742) and T.japonicum (NUBS 13002)— under both in vitro and ex vivo conditions. RNA quality was further verified using the Agilent 2100 Bioanalyzer and the RNA 6000 Nano Kit (Agilent Technologies). Due to moderate RNA degradation (RIN <8), library preparation was adjusted accordingly. Ribosomal RNA was depleted using the NEBNext Poly(A) mRNA Magnetic Isola tion Module (New England Biolabs), and libraries were prepared with the KAPA RNA HyperPrep Kit (Roche). Twenty nanograms of RNA was used for input; fragmentation was performed at 85 °C for 2 minutes, followed by 14 amplification cycles. Indexed adapters (KAPA Dual-Indexed Adapter Kit, Roche) were used, and sequencing was performed on the Illumina NextSeq 500 platform.1 Reads were aligned using STAR v.2.7.1 (35) and mapped with Bowtie2 (36) to a genome index based on T.europaeum strain CBS 112371 (RefSeq: GCF_000151125.1). Differential expression analysis was conducted in R v.3.6.0 using the DESeq2 package (37), with standard error and log₂ fold change computed. The Wald test and BenjaminiHochberg correction were applied. All replicates from both conditions were used. Genes with adjusted P-values < 0.05 were considered significant and annotated using NCBI and UniProt databases. Research Article Microbiology Spectrum November 2025 Volume 13 Issue 11 10.1128/spectrum.01383-25 4 RT-qPCR analysis For RT-qPCR, 1 µg of total RNA was reverse-transcribed using the LunaScript RT SuperMix Kit (New England Biolabs) according to the manufacturer’s protocol. Comple mentary DNA (cDNA) from three high-quality biological replicates was pooled in equal amounts prior to qPCR. This approach was chosen to minimize noise from individuallevel variation and to ensure sufficient cDNA quantity across the large panel of target genes and conditions. We acknowledge that pooling limits the ability to assess withinstrain variability. Quantitative PCR was performed using the CFX96 Real-Time System (Bio-Rad Laboratories) and the Luna Universal qPCR Master Mix (New England Biolabs). The PCR reaction was done with 0.5 µL of 10 pM primers, and 0.5 µL of cDNA in the final volume of 20 µL. The reaction comprised an initial denaturation at 95 °C for 60 s, followed by 39 cycles of 95 °C for 15 s and 60 °C for 30 s, inactivation at 95 °C for 60 s, cooldown at 54 °C for 60 s, and final cooldown at 25 °C for 60 s. Melting curve analysis was conducted by increasing the temperature from 54 °C to 95 °C in 0.5 °C increments every 10 s. Each measurement was performed in duplicate, and if the standard deviation exceeded 2, the experiment was repeated. Primer design and efficiency testing Primer pairs for ARB_00701 and TERG_04919 were adapted from previously published studies (23, 38). All other primers were designed using Primer-BLAST (NCBI) based on the genome sequence of T.europaeum CBS 112371 (RefSeq: GCF_000151125.1). Each primer pair was designed such that at least one primer spanned an exon–exon junction to avoid amplification of genomic DNA. The OligoAnalyzer tool (IDT) was used to assess potential secondary structures, with accepted primers showing ΔG > –7 J for dimers and > –4 J for hairpins. All primer pairs were tested using pooled cDNA and serial dilutions (1:10; 1:50; 1:250; 1:1,250; 1:6,250). Primer efficiencies ranged from 85% to 100% (Supplemental material 2a). RT-qPCR data processing and analysis The stability and reliability of four previously published reference genes were analyzed using RefFinder (39), which integrates the tools BestKeeper (40), NormFinder (41), and geNorm (42). Specifically, the analyzed genes were ARB_06116 (DNA-directed RNA polymerase II subunit RPB2; rpb2) (43), ARB_03667 (ADP-ribosylation factor; adp-rf) (44, 45), ARB_00512 (Mitotic cohesin complex subunit Psm1; psm1) (44, 45), and TERG_04919 (Chitin synthase; CHS) (46). Based on this analysis, rpb2 and adp-rf were selected as the most stable reference genes (Supplemental material 2b). For processing of the acquired RT-qPCR data, the software Bio-Rad CFX Manager v. 3.1 (Bio-Rad Laboratories) was used. Due to the large number of genes examined and the inability to measure the expression of all genes on a single plate for both conditions, the efficiency-weighted method (47) was chosen for data normalization. For the same reason, both reference genes were always run on each plate for all measured samples as an interplate calibrator. The 2-ΔΔCt analysis was performed. As technical replicates were used and cDNA was pooled prior to qPCR, statistical comparisons reflect between-strain variation rather than intra-strain variability. The cycle threshold (Ct) range, response efficiency, coefficient of determination, relative gene expression (ΔCt) values for each strain under different culture conditions, and the effect size measure values (2-ΔΔCt) for individual genes are provided in Supplemental material 3. The normal distribution of data was tested with the use of the Shapiro-Wilk test. Based on the results, either ANOVA or Kruskal-Wallis tests were performed, followed by pairwise t-tests. Results are shown in Supplemental material 4. For the statistical analysis, R v. 4.2.1 (48) was used with the package stats, reshape2 (49), FactoMineR (50), and factoextra (51). The visualization of data was performed with the use of packages from the Tidyverse (52), especially ggplot2 (53), and the packages viridis (54), gridExtra (55), and cowplot (56). Research Article Microbiology Spectrum November 2025 Volume 13 Issue 11 10.1128/spectrum.01383-25 5 Bioinformatic identification of Biosynthetic Gene Clusters Biosynthetic gene clusters (BGCs) in the genome of T.benhamiae were identified using the fungal version of antiSMASH v.7.1.0 (57). To assess the level of similarity of genes or clusters of interest, the Ident and Sim tool (https://www.bioinformatics.org/sms2/ ident_sim.htmL), BLAST v. 2.15.0 (58), and MAFFT (59) were used. The visualization of clusters was performed in R v. 4.2.1 (48) with the gggenes package (60). Histological analysis Prior to RNA extraction, 5 × 5 mm sections of selected skin samples were fixed, embed ded, and stained. Fixation was performed in 4% paraformaldehyde and 10% formalin for 48 h (61). Samples were dehydrated using a Leica ASP200s and embedded in paraffin using a Leica EG110 system. Sections (3 µm) were cut using a Leica RM2125 RTS rotary microtome and placed on precoated slides (Electron Microscopy Sciences). Staining was performed using the standard hematoxylin and eosin (H&E) protocol (62–64), with an additional benzene clearing step. Samples were mounted in Canadian balsam, covered with glass coverslips, and left to harden overnight. H&E-stained sections were used to qualitatively confirm fungal invasion and tissue colonization. No quantitative fungal burden measurement (e.g., by ITS copy number or CFU counts) was performed. RESULTS Selection of target genes A total of 85 genes were significantly differentially expressed (adjusted P < 0.05) between T.benhamiae var. luteum IHEM 25742 and T.japonicum NUBS 13002. The putative functions of more than half were inferred from the predicted protein structure. According to antiSMASH analysis, 11 genes belonged to two biosynthetic gene clusters (BGCs). One cluster was identified as the vioxanthin, xanthomegnin, and viomellein BGC and is referred to here as cluster A; the second, of unknown function, is referred to as cluster B (Table 2; Fig. 1). Additionally, two other genes (ARB_04645, ARB_07966) were identified in the data set (Table 2). ARB_04645 is a component of a previously described ergot alkaloid synthesis pathway in Arthrodermataceae (65). The cluster associated with ARB_07966 could not be identified. Other differentially expressed genes were associated with transmembrane transport, basic cellular functions, cell wall remodeling, and sulfur metabolism (see Supplemental material 1 for the full list of differentially expressed genes). Based on the expression profiles of the 85 significant genes, the samples were divided into two primary clusters that reflected strain identity (Fig. 2a). The first principal component (PC1) accounted for 85% of total variability, separating samples by spe cies, while the second component (PC2) distinguished samples based on cultivation condition. A hierarchical clustering analysis also grouped the samples primarily by species (Fig. 2b). Samples of T.japonicum (NUBS 13002) collected at 6 and 8 days post-inoculation showed minimal variation in gene expression (Fig. 2). From these data, 16 genes representing diverse cellular functions were selected for further investigation across a larger data set of 12 strains from the T.benhamiae complex using RT-qPCR on cDNA pooled from biological replicates (Table 3). Nine genes were selected from the transcriptomic data set—seven of which showed significant interstrain differences, and five were associated with BGCs. Two additional genes (ARB_02741 and ARB_05770) were included due to their presumed functional relevance despite non-significant p-values. Another seven genes were selected based on a literature review as known dermatophyte virulence factors. Functional annotation of selected genes Bioinformatic analysis revealed that six genes from cluster A (ARB_07989–ARB_07994) share strong similarity with a laccase-containing vioxanthin, xanthomegnin, and Research Article Microbiology Spectrum November 2025 Volume 13 Issue 11 10.1128/spectrum.01383-25 6 TABLE 2 Results of RNA-seq analysis performed using the DESeq2 package, comparing T.benhamiae var. luteum and T.japonicuma Locus Log2 fold change Adjusted P-value Annotation NCBI Annotation UniProt Possible function ARB_06975 −13.80 5.27 × 10−12 Hydrophobin, putative Hydrophobin A Surface hydrophobicity, adherence ARB_05303 6.26 5.84 × 10−08 Uncharacterized protein Sulfotransferase family protein Sulfur-related processes ARB_00847 −11.16 3.45 × 10−07 Uncharacterized protein Uncharacterized protein –b ARB_07538 6.84 2.12 × 10−06 FAD binding monooxygenase, putative FAD binding monooxygenase, putative Secondary metabolism, cluster B ARB_03989 −10.49 1.56 × 10−05 Uncharacterized protein Uncharacterized protein – ARB_04141 −8.46 4.18 × 10−05 Uncharacterized protein Uncharacterized protein – ARB_01369 −10.71 7.33 × 10−05 Uncharacterized protein Uncharacterized protein – ARB_07952 −8.10 0.00011882 Uncharacterized protein Uncharacterized protein – ARB_05947 6.72 0.00012002 Uncharacterized protein FAD-binding PCMH-type domain-containing protein Undetermined ARB_07994 6.76 0.00032219 Conidial pigment polyketide synthase PksP/Alb1 Non-reducing polyketide synthase (NscA) Secondary metabolism, cluster A ARB_04859 5.05 0.00033383 Oxalate decarboxylase, putative Probable oxalate decarboxylase Primary metabolism ARB_02085 −9.14 0.00042272 Uncharacterized protein Methyltransferase domain-containing protein Various biochemical reactions ARB_07989 7.98 0.00054269 O-methyltransferase, putative O-methyltransferase, putative Secondary metabolism, cluster A ARB_07993 7.53 0.00054269 Uncharacterized protein Uncharacterized protein Secondary metabolism, cluster A ARB_07534 6.86 0.00066905 LovB-like polyketide synthase, putative Non-reducing polyketide synthase (NscA) Secondary metabolism, cluster B ARB_07518 −9.63 0.00069182 Uncharacterized protein Uncharacterized protein – ARB_04430 −8.78 0.00069182 Uncharacterized protein Uncharacterized protein – ARB_07536 8.80 0.00087444 Uncharacterized protein Probable secreted aspartic protease Secondary metabolism, cluster B ARB_04594 8.38 0.00087444 Uncharacterized protein STAS domain-containing protein Sulfur-related processes ARB_03861 5.11 0.00087739 Glutathione S-transferase Ure2-like, putative Glutathione S-transferase Ure2-like, putative Stress-related processes ARB_07951 −8.30 0.00087739 Uncharacterized protein DUF3328 domain protein Undetermined ARB_06429 −9.81 0.00087739 O-methyltransferase O-methyltransferase Various biochemical reactions ARB_05141 −8.65 0.00087739 Uncharacterized protein Uncharacterized protein – ARB_05392 −7.73 0.00098404 Uncharacterized protein Chitinase Cell wall-related processes ARB_02932 6.79 0.00133298 RTA1 domain protein, putative RTA1 domain protein, putative Undetermined ARB_07276 4.72 0.00157637 Uncharacterized protein phosphoadenosine phosphosulfate reductase domain-containing protein Sulfur-related processes ARB_05274 4.96 0.00195658 Conserved uncharacterized protein Uncharacterized protein – ARB_04644 −5.81 0.00208368 Uncharacterized protein Lumazine-binding protein Undetermined ARB_06173 4.66 0.00208841 Zinc-containing alcohol dehydrogenase, putative Zinc-containing alcohol dehydrogenase, putative Undetermined ARB_01072 7.31 0.00210063 Carboxylesterase, putative Carboxylic ester hydrolase Various biochemical reactions ARB_03721 5.78 0.00256559 Uncharacterized protein AB hydrolase-1 domain-containing protein Undetermined ARB_07517 −7.66 0.00270752 Uncharacterized protein Acyl-protein thioesterase 1 Sulfur-related processes ARB_07246 −9.05 0.00270752 Uncharacterized protein DUF1203 domain protein Undetermined ARB_01258 −3.87 0.00301641 Uncharacterized protein Uncharacterized protein – ARB_01444 −7.01 0.00329636 Uncharacterized protein Glucan endo-1,3-beta-D-glucosidase Cell wall-related processes ARB_07535 7.25 0.00386691 Uncharacterized protein Cytochrome P450 Secondary metabolism, cluster B ARB_05140 −7.40 0.00396502 Uncharacterized protein Protein kinase domain-containing protein Basic cellular processes ARB_01724 −5.24 0.0041556 Hsp90 binding co-chaperone (Sba1), putative Hsp90 binding co-chaperone (Sba1), putative Basic cellular processes ARB_07991 7.48 0.0041556 Fasciclin domain family protein Fasciclin domain family protein Secondary metabolism, cluster A ARB_04955 −7.43 0.0041556 Uncharacterized protein Uncharacterized protein – ARB_01579 −7.22 0.00518502 Uncharacterized protein Uncharacterized protein – ARB_07992 6.66 0.00613564 Short chain dehydrogenase/reductase family protein Short chain dehydrogenase/reductase family protein Secondary metabolism, cluster A ARB_03514 −7.93 0.0069251 Class III chitinase ChiA2 Class III chitinase Cell wall-related processes ARB_00424 3.33 0.0069251 Uncharacterized protein Glutathione transferase Sulfur-related processes ARB_05391 3.76 0.0069251 PTR family peptide transporter, putative PTR family peptide transporter, putative Transmembrane transport ARB_00651 −7.26 0.00707818 Uncharacterized protein HNH nuclease domain-containing protein Undetermined ARB_06241 −6.27 0.00867606 Opsin, putative Opsin, putative Undetermined (Continued on next page) Research Article Microbiology Spectrum November 2025 Volume 13 Issue 11 10.1128/spectrum.01383-25 7 TABLE 2 Results of RNA-seq analysis performed using the DESeq2 package, comparing T.benhamiae var. luteum and T.japonicuma (Continued) Locus Log2 fold change Adjusted P-value Annotation NCBI Annotation UniProt Possible function ARB_05812 4.24 0.00962227 Uncharacterized protein Fatty acid hydroxylase domain-containing protein Primary metabolism ARB_03863 −7.33 0.01095632 Uncharacterized protein Uncharacterized protein – ARB_01940 4.02 0.01095632 Homogentisate 1,2-dioxygenase, putative Homogentisate 1,2-dioxygenase Various biochemical reactions ARB_03395 −4.26 0.01250736 Solid-state culture expressed protein (Aos23), putative Solid-state culture expressed protein (Aos23), putative Undetermined ARB_06838 −6.83 0.01278189 GPI-anchored endo-1,3(4)-beta-glucanase, putative GPI-anchored endo-1,3(4)-beta-glucanase, putative Cell wall-related processes ARB_04645 −6.42 0.01278189 Uncharacterized protein Catalase easC Synthesis of ergot alkaloids ARB_01913 −7.08 0.01288591 Uncharacterized protein Uncharacterized protein – ARB_02208 3.52 0.0133612 Uncharacterized protein Oxalate decarboxylase Primary metabolism ARB_07818 −3.32 0.0133612 Cytochrome P450 monooxygenase, putative Cytochrome P450 monooxygenase, putative Various biochemical reactions ARB_06800 −6.79 0.01489926 Uncharacterized protein Uncharacterized protein – ARB_07966 3.61 0.0151062 Uncharacterized protein Non-reducing polyketide synthase (NscA) Secondary metabolism ARB_01650 −5.56 0.0158866 Uncharacterized protein Uncharacterized protein – ARB_00746 5.89 0.0158866 Uncharacterized protein Major facilitator superfamily (MFS) profile domaincontaining protein Transmembrane transport ARB_04467 −3.58 0.01673777 Glucanase, putative Probable glucan 1,3-beta-glucosidase Cell wall-related processes ARB_04431 −6.92 0.01749836 Uncharacterized protein altered inheritance of mitochondria protein 9, mitochondrial Undetermined ARB_01248 4.09 0.02039071 Zinc-containing alcohol dehydrogenase, putative Zinc-containing alcohol dehydrogenase, putative Undetermined ARB_00248 −6.96 0.02259337 NAD-dependent epimerase/dehydratase family protein NAD-dependent epimerase/dehydratase family protein Undetermined ARB_01027 3.10 0.02563679 Uncharacterized protein MFS-type efflux pump MFS1 Transmembrane transport ARB_07899 −6.84 0.02563679 Folylpolyglutamate synthetase, putative Folylpolyglutamate synthase Basic cellular processes ARB_06251 −7.70 0.02846453 Uncharacterized protein Uncharacterized protein – ARB_04058 −3.95 0.02918407 DNA repair and transcription factor Ada, putative DNA repair and transcription factor Ada, putative Basic cellular processes ARB_07483 −3.10 0.02966119 Heat shock protein Awh11, putative Heat shock protein Awh11, putative Basic cellular processes ARB_03753 5.25 0.02971309 Uncharacterized protein Major facilitator superfamily (MFS) profile domaincontaining protein Transmembrane transport ARB_02282 −6.74 0.03048244 Uncharacterized protein Uncharacterized protein – ARB_06102 −6.47 0.03048244 Uncharacterized protein Aminoglycoside phosphotransferase domaincontaining protein Various biochemical reactions ARB_04769 6.71 0.03048244 Uncharacterized protein Probable neutral protease 2 homolog Secreted protease ARB_05177 −6.92 0.03148407 Uncharacterized protein Upregulated in Daf-2 domain-containing protein Undetermined ARB_03519 5.33 0.03163056 Uncharacterized protein RTA1 domain protein Undetermined ARB_07381 −5.84 0.03260191 Uncharacterized protein Uncharacterized protein – ARB_03167 −6.36 0.03381736 Uncharacterized protein Uncharacterized protein – ARB_07537 6.21 0.03381736 Polyketide synthase, putative Polyketide synthase, putative Secondary metabolism, cluster B ARB_06539 −7.51 0.03381736 Uncharacterized protein Uncharacterized protein – ARB_07990 5.99 0.03754576 Conidial pigment biosynthesis oxidase Arb2 Conidial pigment biosynthesis oxidase Arb2 Secondary metabolism, cluster A ARB_06768 −3.47 0.03754576 Phosphate-repressible phosphate permease, putative Phosphate transporter Transmembrane transport ARB_05610 −6.21 0.03754576 Uncharacterized protein Uncharacterized protein – ARB_03617 −2.59 0.03890598 Uncharacterized protein LEA domain protein Undetermined ARB_05078 −5.59 0.04451843 Uncharacterized protein Alpha-carbonic anhydrase domain-containing protein Undetermined aThe table displays the top 85 statistically significant genes (adjusted P < 0.05). Genes belonging to biosynthetic gene clusters (BGCs) investigated further are labeled as Cluster A or Cluster B. Genes selected for RT-qPCR validation are shown in bold. b"–” indicates not assigned. Research Article Microbiology Spectrum November 2025 Volume 13 Issue 11 10.1128/spectrum.01383-25 8 viomellein BGC from T. rubrum (77, 78) (Fig. 1; Supplemental material 5). The five genes in cluster B (ARB_07534–ARB_07538) could not be linked to any known metabolite. The core gene (ARB_07534) was annotated as either a LovB-like polyketide synthase or a nonreducing polyketide synthase encoded by nscA. However, protein-level identity was only 21% with the lovastatin synthase LovB from A. terreus (ATEG_09961) (80) and 17.64% with the neosartoricin synthase NscA from T. tonsurans (TESG_06702) (81). Based on this low sequence similarity, it is unlikely that ARB_07534 performs the same biosynthetic function as either of these enzymes. RT-qPCR analysis Gene expression under in vitro and ex vivo conditions In both cultivation systems, sample clustering reflected species-level differences, except in a few cases: T.benhamiae var. benhamiae vs. var. luteum under in vitro (SDB) conditions, and T.japonicum under ex vivo (MSE) conditions. In the ex vivo model, the species were more clearly separated, and even the two closely related T.benhamiae varieties were distinctly grouped (Fig. 3). Gene expression was generally higher under ex vivo conditions, but only three genes showed statistically significant differences (Supplemental material 3c and 4). The DPPIV protease gene (ARB_06110) showed significantly different expression between T.benhamiae var. benhamiae and T.japonicum. T.europaeum significantly overexpressed polyketide synthase ARB_07994 (cluster A) compared with T.japonicum. Additionally, FIG 1 Biosynthetic gene clusters (BGCs) related to secondary metabolism as predicted by antiSMASH analysis. (a) Cluster A: biosynthetic gene cluster responsible for the synthesis of vioxanthin, xanthomegnin, and viomellein in Trichophyton rubrum (strain CBS 118892), compared with the genome of T.europaeum (strain CBS 112371); similarity values are based on BLAST analysis. (b) Cluster B: uncharacterized biosynthetic gene cluster in T.europaeum with an unknown product. Genes that showed significant differential expression in RNA-seq analysis are annotated and highlighted with bold outlines. Research Article Microbiology Spectrum November 2025 Volume 13 Issue 11 10.1128/spectrum.01383-25 9 ACKNOWLEDGMENTS The project was supported by the Long-term Research Development Project of the Czech Academy of Sciences (RVO: 61388971) and the project MycoLife – world of fungi, AV21 Strategy program of the Czech Academy of Sciences (VP33). We also acknowl edge support from the project Talking microbes—understanding microbial interactions within One Health framework (CZ.02.01.01/00/22_008/0004597) and Lumina quaeruntur project of the Czech Academy of Sciences (LQ200202501). We thank Milada Chudíčková and Soňa Kajzrová for their valuable assistance in the laboratory. We thank Klára Kostovčíková and Milada Šírová for their kind help with obtaining the murine skin tissue. We thank Jiří Dvořák for his help with an introduction into the RT-qPCR methodology. The research reported in this publication was part of the long-term goals of the ISHAM Onygenales Working Group. AUTHOR AFFILIATIONS 1Laboratory of Fungal Genetics and Metabolism, Institute of Microbiology, Czech Academy of Sciences, Prague, Czech Republic 2Department of Genetics and Microbiology, Faculty of Science, Charles University, Prague, Czech Republic AUTHOR ORCIDs Lenka Machová http://orcid.org/0009-0008-1447-3593 Martin Kostovčík http://orcid.org/0000-0001-6982-1470 Karel Švec http://orcid.org/0000-0002-0077-8539 Miroslav Kolařík http://orcid.org/0000-0003-4016-0335 Adéla Wennrich http://orcid.org/0000-0002-7986-6688 FUNDING Funder Grant(s) Author(s) Ministry of Education CZ.02.01.01/00/22_008/0004597 Adéla Wennrich Czech Academy of Sciences AV21 Program, CAS (VP33) Miroslav Kolařík Czech Academy of Sciences LQ200202501 Vít Hubka Czech Academy of Sciences RVO: 61388971 Miroslav Kolařík AUTHOR CONTRIBUTIONS Lenka Machová, Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Visualization | Martin Kostovčík, Formal analysis, Methodology, Software | Karel Švec, Formal analysis, Methodology, Software | Vít Hubka, Resources | Miroslav Kolařík, Conceptualization | Adéla Wennrich, Conceptualization, Project administration, Resources, Supervision DATA AVAILABILITY The raw RNA sequencing data generated in this study will be deposited in the NCBI Sequence Read Archive (SRA) under the BioProject accesion number PRJNA1328479. Processed gene expression data and RT-qPCR results (including ΔCt and 2–ΔΔCt values) are available in Supplemental material 3 and 4. Additional data supporting the findings— such as primer sequences, gene annotations, and statistical outputs—are provided in the Supplementary Information or are available from the corresponding author upon reasonable request. All fungal strains used in this study are publicly available through the Culture Collection of Fungi (CCF), Charles University, Prague. Research Article Microbiology Spectrum November 2025 Volume 13 Issue 11 10.1128/spectrum.01383-25 16 ADDITIONAL FILES The following material is available online. Supplemental Material Supplemental material 1 (Spectrum01383-25-s0001.xlsx). RNAseq differential expression analysis (DESeq2) comparing T.benhamiae var. luteum and T.japonicum. Supplemental material 2 (Spectrum01383-25-s0002.xlsx). Primer sequences and evaluation of reference gene stability. 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