Introduction

Dermatomycosis, a fungal infection that exclusively grows in keratinized host structures, is reported to affect about 20–25% of the world’s population, and that of the hair, skin, or nails is one of the most common fungal infections worldwide1,2,3,4. In geographic regions with high prevalence, the incidence of dermatomycosis reaches as high as 40–60%5,6,7. Dermatomycosis is difficult to cure and is prone to recurrence and reinfection, which seriously affects the quality of patients’ lives and has significant economic implications. What’s more important, the infection can progress from a localized disease to an invasive infection when the host’s immune system is weakened by various factors8,9. The clinically predominant dermatophyte species causing skin infections is Trichophyton rubrum (T. rubrum), accounting for approximately 80% of dermatomycosis cases10. Clinical drugs commonly used to treat infections were the azole class, triazoles, heterocyclic benzofurans, and allylamines. However, due to the prolonged therapy duration, limited efficacy, emerging antifungal resistance, and adverse effects of available drugs, the clinical treatment of these infections remains challenging11,12.

In recent years, traditional Chinese medicine (TCM) has emerged as a valuable type of complementary and alternative medicine for the treatment of dermatomycosis, due to its broad-spectrum antifungal activity, low incidence of drug resistance, and relatively mild toxicity13,14,15. TCM is one of the oldest medical systems in the world and has proven to be both safe and effective. Huangqin decoction (HQD), a classical TCM formula from Shang Han Lun written by Zhang Zhongjing in the Han dynasty, is an aqueous extract of four herbal materials, including the roots of Scutellaria baicalensis Georgi, Paeonia lactiflora Pall., Glycyrrhiza uralensis Fisch, and the fruit of Ziziphus jujuba Mill. Modern research has demonstrated that HQD could exhibit anti-inflammatory effects16,17 and antifungal properties18. The principal bioactive components of HQD possess diverse pharmacological activities. For example, paeoniflorin19 has anti-inflammatory and immunomodulatory effects. Glycyrrhizin20,21 has antimicrobial properties. Baicalin22, baicalein23 and wogonin23 have antifungal activities. Our previous research demonstrated that HQD could significantly inhibit the growth of T. rubrum, with a minimal inhibitory concentration (MIC) of 3.13 mg/mL24. However, the study utilized several reagents that focus on common enzymes such as squalene epoxidase (SE), sterol 14α-demethylase P450 (CYP51), malate dehydrogenase (MDH), etc., to investigate whether these enzymes in T. rubrum would be affected by HQD. The comprehensive mode of action of HQD against T. rubrum remains unclear.

The transcriptome is the entire set of RNA transcripts from a particular tissue, including messenger RNA, regulating the transmission of genetic information from DNA to protein25,26,27. Transcriptome sequencing can simultaneously detect and quantify almost all transcripts in a species at specific developmental stages or physiological states. The results can display the involvement of differentially expressed genes (DEGs) between healthy and diseased groups, across biological samples with or without drug treatment, which provides an opportunity to elucidate the underlying molecular mechanisms of diseases or drugs at the transcriptome level28,29,30,31,32. In recent years, due to the high throughput and high sensitivity, transcriptome sequencing has made tremendous progress in exploring the mode of action of medicines against fungi or bacteria33,34,35.

In the present study, we elucidated the preliminary mechanisms of HQD against T. rubrum through combining transcriptome sequencing verified by quantitative real-time PCR (qRT-PCR) and enzymatic activity assays, aimed to provide new insights into the therapeutic intervention against dermatophytes. Technically, T. rubrum strains were treated with HQD at a concentration of MIC, and then transcriptome sequencing was performed to identify DGEs in T. rubrum after HQD exposure. Gene function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to reveal the functions and signaling pathways regulated by these DEGs. The transcriptome findings were further validated by qRT-PCR for DEGs of particular interest. Finally, enzymatic activity assays were conducted to explore the potential molecular targets of HQD against T. rubrum.

Materials and methods

Materials

The HQD formula consists of four medical herbs: the roots of Scutellaria baicalensis Georgi (skullcap), Paeonia lactiflora Pall. (peony), Glycyrrhiza uralensis Fisch. (licorice), and the fruit of Ziziphus jujuba Mill. (jujube), in a ratio of 3:2:2:2, respectively. All herbal materials were purchased from Jiangxi Peng’s Guoyaotang Sliced Herb Co., Ltd. and authenticated by Chief Pharmacist Ping Ying from the Department of Pharmacy, Jiangxi Provincial People’s Hospital (the First Affiliated Hospital of Nanchang Medical College), PR China. A filamentous fungus total protein extraction kit was purchased from Beibo Biotechnology Co., Ltd. Aconitate hydratase (ACO) reagent kit was obtained from Shanghai MLBIO Biotechnology Co., Ltd. Citrate synthase (CS) and isocitrate lyase (ICL) reagent kits were bought from Solarbio Co., Ltd.

Preparation of HQD

The preparation of HQD was conducted as previously described in our earlier study36. According to the prescription of HQD, 18 g of skullcap, 12 g of peony, 12 g of licorice, and 12 g of jujube were soaked together in distilled water (1:10, w/v) for 30 min and then decocted for 1 h. After filtration, the residue was boiled in distilled water (1:8, w/v) for another 1 h. The two filtrates were combined, concentrated under reduced pressure at 60 ℃, and freeze-dried at room temperature to obtain 21.25 ± 1.01 g of lyophilized powder.

Fungal strains and HQD treatment

The fungal strain of T. rubrum STRB12 was provided by researcher Ping Zhan, co-author of the reference37. The strain was identified through phenotype characterization, microscopic analysis, and internal transcribed spacer (ITS) sequence comparison37. Prior to experiments, the fungal strains were reactivated and subcultured twice on the Sabouraud Dextrose Agar (SDA: 15 g/L agar, 40 g/L glucose, 10 g/L peptone, 0.125 g/L chloromycetin) to purify the colony and enhance its vitality. Thereafter, T. rubrum was inoculated into SDA and incubated at 28\(\:\pm\:\)2 °C for 7–14 days, and spores were harvested from sporulating colonies and suspended in a sterile 0.85% NaCl solution containing 1% Tween-20. The spore concentrations were determined and adjusted to the inoculum of 5.0 × 105 CFU/mL using a hemocytometer. The working suspension with a final inoculum of 5.0 × 103 CFU/mL was prepared by 100-fold dilution of the inoculum suspension with Sabouraud dextrose broth (SDB: 40 g/L glucose, 10 g/L peptone, 0.125 g/L chloromycetin).

The experimental strain samples were divided into two groups: (1) the HQD-treatment group: three biological replicates (STRB12-HQD1, STRB12-HQD2, STRB12-HQD3) exposed to HQD at the MIC (i.e., 3.13 mg/mL[24]), a stress concentration to elicit the antifungal response in T. rubrum; (2) the control group: three replicates (STRB12-KB1, STRB12-KB2, STRB12-KB3) treated with an equal volume of SDB. After 5 days of incubation at 28 °C, the mycelia were harvested by sterile Whatman paper, washed twice with sterile distilled water, and dried on filter paper for RNA extraction.

Transcriptome sequencing

RNA extraction and library construction for sequencing

Total RNA was isolated from the tissues using Trizol (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s instructions and the protocol described in reference34. RNA concentration was measured using a Qubit 4.0 fluorometer, while RNA integrity was assessed with an Agilent Bioanalyzer 2100 system (Agilent Technologies, MA, USA), and RNA purity was determined using a NanoDrop 2000 (Thermo Fisher Scientific, MA, USA). After the total RNA passed quality control, Oligo(dT)-attached magnetic beads were used to purify the eukaryotic messenger RNA (mRNA). Purified mRNA was fragmented into short fragments with RNA fragmentation buffer. Using fragmented mRNA as a template, the first-strand complementary DNA (cDNA) was generated using random hexamer-primed reverse transcription, followed by a second-strand cDNA synthesis. Hieff NGS®DNA Selection Beads were used to purify double-stranded products and perform fragment selection. Finally, PCR amplification was performed to obtain the final library. After the library was qualified, the DNBSEQ-T7 was used for paired-end sequencing.

Sequencing data analysis

The sequencing data were filtered with SOAPnuke (v1.5.2)38 by removing reads containing adapters and poly-N sequences, as well as low-quality reads or those with uncertain bases, to obtain clean reads. Meanwhile, the quality metrics of the clean data, including Q20, Q30, GC-content, and the sequence duplication level, were determined. Finally, the clean reads were aligned to the reference genome sequence (GenBank assembly of GCA_000151425.1, species of Trichophyton rubrum CBS 118892, genome size of 22.5 Mb) using the Hisat2 (v2.0.4)39 tools. Data from all the sequencing are deposited in the NCBI Gene Expression Omnibus (GEO) database40 under the accession number GSE300994.

Detection of differentially expressed genes

The expression level for each gene was quantified using the FPKM (Fragments per kilobase of transcript per million fragments mapped) method implemented in StringTie (v2.1.2)41. The differences of gene expression profiles between the HQD-treatment group and the control group were calculated and compared by the DESeq2 R package (v1.4.5)42. The statistical p-values across the two groups were adjusted using the Benjamini-Hochberg method to control the false discovery rate (FDR)43. A cut-off threshold of |log2(fold change)| (|log2(FC)|) \(\:\ge\:\:\)1 and FDR-adjusted p-value (also called q-value) < 0.05 was applied to define up-regulated and down-regulated transcripts. Visualization of the information on the differentially expressed genes (DEGs) was performed by the R package (v3.6.1), including a volcano plot, a correlation plot, a bar plot, and a cluster heatmap.

Gene function analysis and KEGG pathway enrichment analysis

Gene function analysis was conducted to ascertain the potential functions of total DEGs, ranging from cellular processes to organismal functions and ecosystem interactions. In this study, the Clusters of Orthologous Genes (COG) analysis and Gene Ontology (GO) analysis were performed based on the homologous classification of gene products in the COG database44 and ontologies of gene products in the GO database45, respectively. Pathway classification analysis was conducted to identify high-level functions and utilities of the biological system, such as metabolic pathways or signaling transduction pathways, using the KEGG database46,47,48. The KEGG pathway enrichment of the DEGs was performed using KOBAS software (v3.0)49, and pathways with a statistical q-value < 0.05 were assigned as significantly enriched.

The qRT-PCR validation

The expression levels of six DEGs in T. rubrum affected by HQD, as identified in the transcriptome sequencing results, were further verified by qRT-PCR. Fungal suspension preparation and HQD treatment followed the protocol described in the ‘Fungal strains and HQD treatment’ section. Three independent fungal samples from both the HQD-treatment group and the control group were used for qRT-PCR analysis. Technically, 1 µL of total RNA from each sample was reverse-transcribed into first-strand cDNA using SynScript®ⅢRT SuperMix according to the manufacturer’s instructions. The cDNA was diluted 3-fold as a qPCR template. Quantitative RT-PCR experiments were performed using the ABI QuantStudio StepOne Plus system with ArtiCanCEO SYBR qPCR Mix according to the manufacturer’s protocol. The thermal cycling conditions were initial denaturation at 95 °C for 5 min, followed by 40 cycles of 94 °C for 15 s, 60 °C for 20 s, and 72 °C for 20 s for annealing, and then an extension. For each test gene, the reactions were performed in triplicate.

The 2−ΔΔCt method50 was used to quantify the relative expression level of each gene, with 18 S rRNA as the endogenous control. The primers for the examined genes were listed in Table 1. Statistical difference across groups was analyzed by Multiple t-tests using GraphPad Prism software (v6.01), and the results for each gene were presented as the mean ± standard deviations. Genes with a p-value < 0.05 were considered significantly differentially expressed.

Table 1 The primers of examined genes applied in qRT-PCR assay.
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Enzyme activity assays

The enzyme activities of three key enzymes involved in the glyoxylate cycle, including citrate synthase (CS), aconitate hydratase (ACO), and isocitrate lyase (ICL), were further identified. Fungal suspension preparation and HQD treatment followed the protocol described in the ‘Fungal strains and HQD treatment’ section. Mitochondria were isolated from T. rubrum mycelia for the activity assays of ACO and CS. Approximately 0.05 g of wet mycelia was weighed, and the corresponding reagents were added to prepare the homogenization. The homogenization medium was centrifuged at 600 g for 5 min at 4 °C to remove the cell debris and unbroken cells. The supernatant was collected and centrifuged at 11 000 g for 10 min at 4 °C. The resulting pellet was collected as the mitochondria fraction, resuspended in the corresponding reagents, and used for measuring protein concentrations and enzymatic activities. For the ICL enzyme activity assay, the working solution and reagents were strictly configured according to the kit instructions. Approximately 0.05 g of wet mycelia was weighed, and 1 mL of the extract reagent was added to prepare the homogenization. The homogenization medium was centrifuged at 15 000 g for 20 min at 4 °C. The supernatant was collected as the sample for testing, mixed with the working solution and corresponding reagents, and used for measuring protein concentrations and enzymatic activities.

The activities of ICL, ACO, and CS were measured in strict accordance with the protocols provided in their respective reagent kits. Each experiment was repeated three times to ensure reproducibility. The activities of ICL and CS were reported in U mg− 1 protein, and that of ACO was expressed as nmol min− 1 mg− 1 protein. Statistical differences between the HQD-treatment group and the control group were assessed using a Multiple t-test using GraphPad Prism software (v6.01).

Results

Sequencing data quality and alignment statistics

The fungal samples subjected to transcriptome sequencing were divided into two groups: the HQD-treatment group and the control group, with three biological replicates for each group. Across these six samples, a total of 41.79 GB of clean data was generated. Following the alignment of the clean reads to the reference genome, the mapped reads were found to range from 88.52% to 98.07%. Additionally, the average proportion of Q30 across all six samples exceeded 95.31%. These results collectively confirm that the sequencing data were of high quality and met the requirements for subsequent analyses, as presented in Table 2.

Table 2 The quality control statistics of sequencing data.
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Differential expression genes in T. rubrum after HQD exposure

To identify DEGs in T. rubrum in response to HQD, the gene expression levels in T. rubrum were compared between the HQD-treatment group and the control group. As visualized by the volcano plot, correlation plot, and bar plot in Fig. 1, a total of 338 genes in T. rubrum exhibited significant expression changes after HQD exposure (q-value < 0.05), of which 199 genes were up-regulated, and 139 genes were down-regulated. Hierarchical clustering analysis was performed on these DEGs, and results from the cluster heatmap in Fig. 1 revealed that expression profiles of DEGs were similar within the same group but distinct between different groups. This observation indicated that the sample repeatability was good and that HQD effectively modulates the gene expression profiles of T. rubrum. These DEGs serve as the key to exploring the antifungal mechanism of HQD against this pathogen.

Fig. 1
Fig. 1
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Differentially expressed genes in T. rubrum affected by HQD. In the volcano plot (A) and correlation plot (B), blue dots represent down-regulated DEGs, orange dots refer to up-regulated DEGs, and green dots indicate non-DEGs. In the bar plot (C), the blue and orange bars display the number of down- and up-regulated DEGs, respectively. In the cluster heat map (D), each row is a DEG.

Functional analysis of DEGs in T. rubrum in response to HQD

To investigate the biological functions in T. rubrum potentially affected by HQD, a total of 338 DEGs were mapped into the COG and GO databases. Through COG annotation, COG functional categories were predominantly recognized as “carbohydrate transport and metabolism” and “energy production and conversion”, as shown in Fig. 2A. Through GO annotation, DEGs were annotated from 3 perspectives: biological process (BP), cellular component (CC), and molecular function (MF), as shown in Fig. 2B. Specifically, in the BP category, DEGs were enriched in “metabolic process” and “cellular process”. In the CC category, the “membrane”, “membrane part”, and “cell” demonstrated the most significant enrichment of DEGs. In the MF category, DEGs were abundant in “catalytic activity” and “binding”. Detailed information on expression values and gene annotations for the DEGs is provided in the Additional file 1.

Fig. 2
Fig. 2
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(A) Cluster of orthologous groups classification of DEGs in T. rubrum affected by HQD. The x-axis indicates 25 functional COG categories. (B) GO analysis of DEGs in T. rubrum in response to HQD. The y-axis is shown in log10-scale.

The KEGG enrichment results revealed that DEGs were enriched in 47 signaling pathways, with the top 20 pathways visualized in Fig. 3. Notably, the most prominent DEGs were highly enriched (q-value < 0.05) in 6 pathways, which are listed in ascending order of q-value as follows: “valine, leucine and isoleucine degradation”, “indole alkaloid biosynthesis”, “ABC transporters”, “synthesis and degradation of ketone bodies”, “starch and sucrose metabolism”, and “glyoxylate and dicarboxylate metabolism”. Among these six pathways, three were identified as being closely related to energy metabolism, namely “valine, leucine and isoleucine degradation”, “synthesis and degradation of ketone bodies”, and “glyoxylate and dicarboxylate metabolism”. This finding was aligned with the enrichment of DEGs in GO terms related to “metabolic process” and “catalytic activity,” as well as the predominance of COG functional categories in “carbohydrate transport and metabolism” and “energy production and conversion”. Detailed information on the enriched pathways of DEGs is provided in the Additional file 2.

Fig. 3
Fig. 3
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The top 20 KEGG pathways enriched by DEGs in T. rubrum affected by HQD. The greater the rich factor, the higher the degree of enrichment. Pathways with a q-value smaller than 0.05 are assigned significantly enriched.

Exposure to HQD resulted in significant down-regulation of all DEGs in these three pathways (q-value < 0.05), as indicated by the pathway-associated circles in Fig. 3. As shown in Fig. 4, the 11 affected DEGs encoded key metabolic enzymes, including citrate synthase (TERG_01272, CS), aconitate hydratase (TERG_01076, ACO), isocitrate lyase (TERG_01271, ICL), dihydrolipoyl transacylase (TERG_07318, DBT), 3-oxoacid CoA-transferase (TERG_01052, OXCT), hydroxymethylglutaryl-CoA lyase (TERG_03476, HMGCL), acetyl-CoA acetyltransferase (TERG_01051, ACAT), and four additional genes (TERG_02836, TERG_04293, TERG_04294, and TERG_06712).

Fig. 4
Fig. 4
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DEGs in T. rubrum related to energy metabolism affected by HQD. Substances in the boxes drawn with a solid line represent the nutrients acquired from the host, while those in the box drawn with a dashed line represent glucose generated from other carbon sources. The DEGs marked with arrows were genes affected by HQD. The figure was based on the pathway maps of map00280, map00630 and map00020 in the KEGG database48 (www.kegg.jp/kegg/kegg1.html).

qRT-PCR validation of DEGs in T. rubrum

To validate the transcriptome sequencing results, six DEGs from the above genes were chosen for expression verification by qRT-PCR assay using independent fungal samples in the presence or absence of HQD. These DEGs were functionally associated with energy metabolism, including three involved in leucine degradation (DBT, ACAT, and HMGCL) and three in the glyoxylate cycle (CS, ACO, and ICL). Detailed information on these DEGs is provided in Table 3.

Table 3 Information on DEGs in response to HQD identified by qRT-PCR assay.
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Each sample gene (including the reference gene) exhibited a single, sharp, and reproducible peak in its melting curve in both the HQD-treated and control groups, with no additional peaks or broad signals indicative of non-specific amplification or primer dimers, as depicted in the Additional Fig. 1. Technical replicates for each gene showed highly concordant melting temperatures (Tm), ranging from ~ 80 °C to ~ 87 °C. These results confirmed the specificity and reproducibility of the qRT-PCR assays and the reliability of our gene expression data.

As shown in Fig. 5A, after HQD treatment, the expression levels of DBT, OXCT, HMGCL, CS, ACO, and ICL were predominantly downregulated in T. rubrum (p-value < 0.05). The qRT-PCR results demonstrated that the expression patterns of the selected DEGs highly matched those of the FPKM values derived from transcriptome sequencing under identical treatment conditions. This consistency confirms the reliability of the transcriptome sequencing data and further supports the conclusion that HQD interfered with the energy metabolism of T. rubrum.

Fig. 5
Fig. 5
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(A) Relative gene expression levels of six DEGs identified by qRT-PCR. (B) Effect of HQD on the activities of ACO, CS, and ICL in T. rubrum. The activities of ICL and CS are expressed as U mg-1 protein, and that of ACO is shown as nmol min-1 mg-1 protein. The y-axis is shown in log2-scale. In each sub-figure, the bars represent the standard deviations of the data. Statistical significance was determined by: ** indicates p-value < 0.01; *** indicates p-value < 0.001.

Effects of HQD on enzymes involved in the glyoxylate cycle of T. rubrum

Given that the glyoxylate cycle represents an important specific metabolic process in fungi and is absent in humans, three enzymes involved in the cycle – CS, ACO, and ICL – were further investigated by considering their potential as antifungal targets. The enzymatic activities of these enzymes were measured in T. rubrum mycelia from both HQD-treatment and control groups. Specifically, ICL activity was assayed in mycelial extracts, whereas CS and ACO activities were determined in the mitochondrial fractions. The results demonstrated that exposure to HQD significantly reduced the activities of CS, ACO, and ICL in T. rubrum (p-value < 0.05). As shown in Fig. 5B, in the absence of HQD treatment, the activities of CS, ACO, and ICL reached 117.36 ± 13.29 U mg− 1, 12.22 ± 1.98 nmol min− 1 mg− 1, and 9.73 ± 0.70 U mg− 1 protein, respectively, which were significantly higher than those in the HQD-treated group (p-value < 0.05). When T. rubrum was treated with HQD at MIC, the activities of CS, ACO, and ICL in T. rubrum were reduced to 32.75 ± 4.60 U mg− 1, 7.49 ± 0.40 nmol min− 1 mg− 1, and 5.55 ± 0.82 U mg− 1 protein, respectively. This corresponded to inhibition rates of 72.09%, 38.71%, and 42.96% compared to the control group.

Table 4 summarizes our main findings in an attempt to correlate the expression levels of genes involved in the glyoxylate cycle obtained by RNA sequencing with those obtained by qRT-PCR, as well as with the corresponding enzyme activities, in T. rubrum strains treated with or without HQD.

Table 4 Gene expression, relative mRNA expression and enzymatic activity of genes of the glyoxylate cycle in strains with or without HQD treatment.
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Discussion

T. rubrum is the primary pathogenic fungus that can adapt to invade keratinized tissues such as skin, hair, and nails, and thus cause a wide spectrum of diseases medically known as dermatophytosis51, tinea52, or onychomycosis53. Current clinical drugs commonly used to treat infections caused by T. rubrum are often limited by the prolonged treatment duration, insufficient efficacy, antifungal resistance, and side effects. HQD, a classical TCM formulation, presents a promising alternative with its documented broad-spectrum antifungal activity, low incidence of drug resistance, and favorable safety profile15. In the present study, we preliminarily elucidate the mode of action of HQD against T. rubrum as a whole by integrating transcriptome sequencing validated by qRT-PCR and enzymatic activity assays. The findings suggested that the mode of action of HQD against T. rubrum may be related to the interference of the energy metabolism in T. rubrum, with citrate synthase emerging as a potential target of HQD.

Pathways related to energy metabolism contribute to metabolic flexibility, permitting microbial cells to survive in a nutrient-limited host environment during infection. During infection, T. rubrum specifically infects epidermal structures rich in keratin and lipids54. Under the glucose-limited conditions, fungal metabolism relies on carbon acquisition from the alternative nutritional sources55,56,57, which are metabolized to the central metabolite acetyl-CoA to fuel the glyoxylate cycle and downstream gluconeogenesis for energy and glucose production58. Amino acid metabolism, particularly the degradation of branched-chain amino acids (e.g., valine, leucine, and isoleucine), served as a major pathway to produce acetyl-CoA in fungi59. The glyoxylate cycle bypasses two decarboxylation steps in the tricarboxylic acid (TCA) cycle, thereby permitting the net conversion of compounds with two-carbon atoms (especially acetyl-CoA) to metabolic intermediates that can be utilized to generate glucose60,61,62.

Transcriptional analyses of T. rubrum revealed the extensive metabolic reprogramming in response to different growth conditions. When grown on proteins (keratin-soy media63, powdered keratin64, and powdered ox hull keratin65, when co-cultured with human keratinocytes66, or when exposed to a sublethal dose of cytotoxic drugs55 and antifungal agents33, key enzymes of the glyoxylate cycle in T. rubrum demonstrated to be highly active. This adaptive change highlights the glyoxylate cycle as a potential pathway for antifungal drug development61.

Notably, the glyoxylate cycle, a fungal primary metabolic process, is absent in humans. Among the metabolic enzymes involved in this cycle, ICL is an exclusive and essential component of this metabolic route. The relation between enzyme ICL and the virulence of various fungi has been well established in species such as Candida albicans67, Candida glabrata68, and Budding Yeast69. For instance, C. albicans mutant strains lacking the gene encoding ICL exhibit reduced virulence in a mouse model70. Similarly, disruption of the ICL1 gene in C. glabrata conferred a severe attenuation of virulence in a murine model of invasive candidiasis68. Building on these findings, we hypothesize that ICL may serve as a potential antifungal target of HQD against T. rubrum. The precise role of ICL in the metabolic flexibility and virulence of T. rubrum will be fully elucidated in our subsequent studies using gene knock-out experiments.

Given the abundance of novel scaffolds inherent in natural products derived from the TCM formulas, the systematic analysis of individual components of the HQD offers valuable insights for the development of new small-molecule antifungal agents. Comprehensive information on the chemical constituents of HQD can be retrieved from public databases, including TCMSID71, ETCM v2.072, and TCMbank73. Beyond these herbal repositories, the current scientific knowledge regarding the composition of HQD has been documented in reference74. As outlined in the Introduction section, several ingredients — including baicalin22, baicalein23, and wogonin23 — have been demonstrated to exert antifungal activity and thus hold potential as potent antifungal agents. These three compounds have been reported to exhibit antifungal activity, but their efficacy against T. rubrum has not yet been confirmed. We hope the findings will facilitate relevant research by other scientists.

Conclusion

In summary, our study integrated transcriptome sequencing, qRT-PCR, and enzyme activity assays to elucidate the antifungal mechanism of HQD against T. rubrum. The majority of the expression-changed genes were identified as being involved in energy metabolism. The expression levels of genes and the enzymatic activities of three enzymes involved in the glyoxylate cycle were dramatically changed upon HQD treatment, highlighting their crucial roles in the antifungal activity. Our study provided the first comprehensive exploration of the mechanisms involved in the exposure of T. rubrum to HQD. The potential of HQD as a therapeutic intervention against dermatophytes was confirmed. Future research should focus on the biological validation of HQD targeting the ICL of T. rubrum by using knock-out methods.