Molecular characterization of Echinocandin resistance and the CHS3-mediated adaptive response in Candida glabrata bloodstream infections in Eastern China

Abstract

Background

Candidemia caused by Candida glabrata is a serious fungal infection, and rising echinocandin resistance presents a significant clinical challenge. Understanding the drug susceptibility profiles, molecular epidemiology, and mechanisms underlying adaptive echinocandin resistance in C. glabrata is crucial.

Results

A total of 106 C. glabrata strains were isolated from blood cultures of 103 candidemia patients across three medical centers in eastern China. Transcriptome sequencing and whole-genome sequence analysis were used to explore the genomic characteristics of echinocandin-resistant strains. Multi-locus sequence typing (MLST) categorized the isolates into 11 sequence types (STs), with ST7 being the most prevalent (67.9%). Drug susceptibility testing revealed a fluconazole resistance rate of 21.7%, while non-wild-type rates for voriconazole, itraconazole, and posaconazole were 23.6%, 7.5%, and 6.6%, respectively. One isolate (Q2-2) was resistant to all three echinocandins. Two isolates were resistant to micafungin and anidulafungin, respectively. Compared to the echinocandin-sensitive strains, the expression of the Chitin synthetase 3 (CHS3) gene was significantly upregulated in echinocandin-resistant strains. Functional analysis of a CHS3-overexpressing strain (ATCC2001-CHS3-OE), generated through homologous recombination, confirmed echinocandin resistance. Conversely, a CHS3 knockout strain (Q2-2-CHS3Δ) exhibited susceptibility to echinocandins.

Conclusions

Our findings suggest that CHS3 plays a critical compensatory role in echinocandin resistance in C. glabrata, offering a promising target for developing future antifungal strategies.

Background

The incidence of Candida glabrata isolated from candidemia has increased in recent years, particularly among patients with hematologic malignancies, solid organ transplants, and those in intensive care units (ICUs) [1,2,3]. In some countries, C. glabrata has surpassed C. albicans to become the second most common cause of candidemia [4,5,6]. Bloodstream infections due to C. glabrata are associated with high mortality and poor prognosis, making them a significant global public health concern. Risk factors for candidemia include surgery, mechanical ventilation, broad-spectrum antibiotic exposure, tuberculosis infection, tumors, and diabetes [7]. Given these factors, timely and appropriate antifungal therapy is typically administered to improve patient outcomes. Consequently, understanding regional and local epidemiology and antifungal susceptibility data is crucial for determining optimal treatment strategies.

C. glabrata has been reported to exhibit reduced susceptibility to azoles, particularly fluconazole and voriconazole [8], even when echinocandins are used as a first-line treatment [4, 6]. In some regions, C. glabrata isolates with non-susceptibility to echinocandins have risen to approximately 10% [9]. Notably, C. glabrata can gradually develop resistance to echinocandins during therapy [10], likely due to its haploid nature and genomic mutations [11], presenting significant challenges for clinical management. Although echinocandin resistance is increasing globally, isolates with such resistance have not yet been reported from eastern China. Consequently, there is an urgent need to investigate the mechanisms underlying C. glabrata resistance to echinocandins to establish a reliable foundation for clinical treatment. One of the mechanisms of acquired echinocandin resistance is believed to primarily involve mutations in the hotspot region of FKS, resulting in protein amino acid replacements [12]. In C. albicans exposed to echinocandin, it has been reported that Chitin synthetase 3 (CHS3) mediates a compensatory increase in chitin content within the cell wall, which exhibits structural deficiencies due to the actions of these drugs [13]. CHS3 plays an important role in elevating cell wall chitin levels and reducing susceptibility to caspofungin. Here, we hypothesized that CHS3 overexpression or mutation contributes to echinocandin resistance in C. glabrata through compensatory cell wall reinforcement.

In this study, the epidemiological characteristics, drug susceptibility, and molecular types of C. glabrata isolated from multiple medical centers in eastern China were analyzed. Whole-genome sequencing and transcriptome sequencing analyses were performed on both echinocandin-susceptible and echinocandin-resistant C. glabrata isolates. Furthermore, the mechanisms underlying echinocandin resistance in C. glabrata were investigated. The aim of this study was to elucidate the most recent epidemiological characteristics of C. glabrata infections and to inform effective management strategies for C. glabrata treatment.

Methods

Strain collection and identification

A total of 106 C. glabrata strains isolated from the blood cultures of 103 candidemia patients were collected from three medical centers in eastern China between September 2017 and August 2021. Notably, echinocandin-resistant C. glabrata emerged during antifungal therapy in three patients with persistent bloodstream infections. Pre-treatment echinocandin-susceptible and post-treatment echinocandin-resistant isolates were collected from their blood cultures.

The strains were grown on SDA at 35 °C for 18–24 h. Strains were identified via a combination of colony morphological observation and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) (Biotyper 3.1 MSP 5989, Bruker Daltonics Company, Germany).

Clinical data

Clinical information was collected for patients with candidemia. The collected data included patient demographics (age, sex, and department distribution), underlying diseases (solid organ malignancies, diabetes mellitus, hematologic disease, cardiovascular diseases, respiratory diseases, intestinal diseases, urinary system diseases), ICU admission, prior broad-spectrum antibiotic administration, prior antifungal drug administration, prior surgeries, receipt of chemotherapeutic drugs, mechanical ventilation, venous catheterization, parenteral nutrition, fever, and septic shock. The study was approved by the Ethics Committee of the Affiliated Hospital of Qingdao University (QYFY WZLL 29598).

Multi-locus sequence typing (MLST) of C. glabrata

Genomic DNA was extracted using the Ezup Column Fungi Genomic DNA Purification Kit (Sangon Biotech, Shanghai, China). C. glabrata genotyping employed the MLST scheme established by Dodgson et al. [14]. Six housekeeping genes (FKS, LEU2, NMT1, TRP1, UGP1, and URA3) in the C. glabrata genome were amplified and sequenced using an ABI 3730XL system (Applied Biosystems, Foster City, CA) according to the international reference MLST scheme. The primer sequences and the conditions used for the sequencing of the six housekeeping genes are shown in Table S1. The nucleotide sequence of each housekeeping gene was compared to that in the MLST database (https://pubmlst.org/organisms/candida-glabrata), and alleles were assigned for each locus. The sequence types (STs) were defined according to the allelic profiles of the isolates.

Antifungal drug susceptibility testing

The minimum inhibitory concentrations (MICs) of the antifungal drugs against C. glabrata were determined using Sensititre YeastOne (Thermo Fisher Scientific, Waltham, MA, USA). The antifungal drugs evaluated and their concentration distributions are shown in Table S2. Echinocandin and fluconazole susceptibility were interpreted according to Clinical and Laboratory Standards Institute (CLSI) M27-ED3 guidelines. Intermediate MIC values were defined as 0.12 µg/mL for micafungin and 0.25 µg/mL for anidulafungin and caspofungin. The fluconazole Susceptibility Dose-Dependent (SDD) value was set at 32 µg/mL. Resistance was defined as a MIC ≥ 0.25 µg/mL for micafungin and a MIC ≥ 0.5 µg/mL for anidulafungin and caspofungin. The epidemiological cutoff values (ECVs) proposed in the CLSI M57-ED4 were used for amphotericin B (AMB; 2 µg/mL), voriconazole (0.25 µg/mL), posaconazole (1 µg/mL), and itraconazole (4 µg/mL). The ECV criterion for 5-fluorocytosine was 0.5 µg/mL, as previously determined [15]. C. krusei ATCC 6258 and C. parapsilosis ATCC 22,019 were used as quality control strains.

Transcriptome sequencing

Transcriptome analysis and whole-genome sequence analysis were performed by Novogene Corporation (Novogene, China). RNA was extracted using the Polysaccharide Polyphenol Plant Total RNA Extraction Kit (TIANGEN, DP441). RNA-seq libraries were generated with a Fast RNA-seq Lib Prep Kit V2 (Cat. No. Rk20306) and sequenced on an Illumina Nova X Plus Series PE150 platform (Novogene, China). The C. glabrata reference genome (CBS 138) from the Candida Genome Database (https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/010/111/755/GCA_010111755.1_ASM1011175v1/GCA_010111755.1_ASM1011175v1_genomic.fna.gz) served as a reference in this study.

RNA integrity was assessed on the 2100 Bioanalyzer using the RNA Nano 6000 Assay Kit (Agilent Technologies, CA, USA). mRNA was purified from total RNA using poly-T oligo-attached magnetic beads and fragmented using divalent cations under elevated temperature in 5× First Strand Synthesis Reaction Buffer. First-strand cDNA was synthesized using random hexamers and M-MuLV Reverse Transcriptase (RNase H), followed by second-strand cDNA synthesis using DNA Polymerase I and RNase H. The remaining overhangs were then converted to blunt ends via exonuclease/polymerase activities. After the adenylation of the 3ʹ ends, adaptors with a hairpin loop structure were ligated to the cDNA in preparation for hybridization. PCR was subsequently performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers, and Index (X) Primers. Finally, the PCR products were purified employing the AMPure XP system, and library quality was assessed on the Agilent 2100 Bioanalyzer system. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using the TruSeq PE Cluster Kit v3-cBot-HS (Illumina). After clustering, the libraries were sequenced on an Illumina Novaseq platform, generating 150-bp paired-end reads. Multiple testing correction was applied to control the FDR [16]. The software used for analysis is shown in Table S3.

Whole-genome sequence analysis

The whole-genome sequences of the C. glabrata isolates were compared against the C. glabrata reference genome (CBS 138). Briefly, for DNA library preparation, genomic DNA (0.2 µg per sample) was fragmented by sonication (Covaris, PerkinElmer, US) to a size of approximately 350 bp. The resulting DNA fragments were end-polished, and A-tailed, followed by the ligation of a full-length adapter for Illumina sequencing, further size selection, and PCR amplification. Subsequently, library quality was assessed on the Agilent 5400 system (Agilent) and quantified by qPCR (1.5 nM).

The qualified libraries were pooled and paired-end (150 bp) sequenced on an Illumina platform, according to effective library concentration and the required data output. Single nucleotide polymorphisms (SNPs), copy number variations (CNVs), insertions and deletions (InDels), and structural variations (SVs) were assessed to determine genetic variation. GATK was used for the detection of individual SNPs and InDels. BreakDancer software was employed for the detection of insertions (INSs), deletions (DELs), inversions (INVs), intra-chromosomal translocations (ITXs), and inter-chromosomal translocations (CTXs) in the samples. ANNOVAR was used to annotate the detected DELs, INSs, and INVs. The Integrative Genomics Viewer (IGV) browser was used for the visual inspection of the BAM file. The analysis software used is shown in Table S4.

RT-PCR

RNA was prepared using the EZ-10 Spin Column Total RNA Isolation Kit (Sangon Biotech). RT-PCR was performed on a T100 Thermal Cycler (BIO-RAD, USA). The RDN5.8 gene served as the internal reference [17]. The relative expression of the CHS3 gene was calculated using the 2−ΔΔCt method. The reaction system and the cycling program used for RT-PCR are shown in Table S5 and Table S6.

Construction of CHS3 knockout and overexpression mutants

Two C. glabrata strains—ATCC2001-CHS3-OE (CHS3-overexpressing) and Q2-2-CHS3Δ (CHS3-knockout)—were generated by Shanghai Qingpu Biotechnology Co., Ltd. CHS3 gene knockout was performed based on homologous recombination. The 3ʹUTR and the 5ʹUTR of CHS3 were used as homology arms to replace the entire open reading frame of CHS3 with a selection marker gene (SAT1). The successful generation of the CHS3 knockout strain was verified using specific diagnostic primers. The relevant schematic diagram is shown in Figure S1. The CHS3-overexpressing strain was also generated via homologous recombination. The promoter of CHS3 (pCHS3) was replaced by the promoter of TDH3 (pTDH3), which is strongly expressed in Candida, to achieve the overexpression of CHS3. SAT1 served as the selection marker. The relevant schematic diagram is shown in Figure S2.

Cell wall calcofluor white (CFW) staining and fluorescence microscopy

The various strains were incubated on Sabouraud agar plates at 35 °C for 18–24 h. Each isolate was stained with CFW for 5 min in the dark and subsequently imaged under a Nikon CI-E fluorescence microscope (Nikon Instruments Inc., Tokyo, Japan) equipped with a MODEL ECLIPSE Ci digital camera. Cell wall fluorescence intensity was analyzed using ImageJ software, and the data represent average fluorescence values.

Statistical analysis

IBM SPSS 22.0 software was used for statistical analysis. The chi-square test was used for comparisons between groups, and non-normally distributed quantitative data were described using the median and interquartile ranges. P ≤ 0.05 was considered significant. To exclude the accumulation of false positives due to the very high number of genomes in the RNA-seq analysis,|log2(FoldChange)| ≥1 (≥ 2-fold for gene expression) or padj ≤ 0.05 was applied as the criterion for differential gene expression. Differential gene expression analysis was performed using DESeq2 (v.1.20.0).

Results

Clinical epidemiological features in patients with C. glabrata bloodstream infection

A total of 106 non-duplicated C. glabrata isolates were collected from 103 patients aged over 4 years. Among these patients, 61 were male (59.2%) and 42 were female (40.8%). The clinical epidemiological features are presented in Table 1. Patient ages ranged from 5 to 92 years, with a median of 62 years (interquartile range: 50.5–72). Most of the patients with C. glabrata infection were older adults aged 51–80 years, accounting for 68.0% (70/103) of all the patients (Fig. 1A). Regarding the departmental distribution of the 103 patients with C. glabrata bloodstream infection, the biggest proportion (42.7%, 44/103) was in surgical departments (General surgery, Cardiac surgery, Hepatobiliary surgery, and Urology surgery), followed by the ICU (24.3%, 25/103), and internal medicine departments (22.3%, 23/103) (Fig. 1B).

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Most of the patients were diagnosed with more than one underlying disease. The most common diseases were malignancies (56.3%, 58/103), including solid organ tumors (30.1%, 31/103) and hematological malignancies (26.2%, 27/103). Hypertension (29.1%, 30/103), pulmonary disease (31.1%, 32/103), diabetes mellitus (28.2%, 29/103), gastrointestinal disease (25.2%, 26/103), renal dysfunction (21.4%, 22/103), and prior surgery (62.1%, 64/103) were also observed. Among all the patients, 40.8% (42/103) had received chemotherapy, 89.3% (92/103) had received antibiotics, and 26.2% had received antifungal drugs before the detection of C. glabrata in their blood cultures. Nearly half of the patients (47.6%) with C. glabrata bloodstream infection had been admitted to the ICU within the last 30 days (Table 1).

Multi-locus sequence typing and antifungal susceptibility testing in C. glabrata isolates

MLST of the 106 isolates revealed 11 distinct STs, including two novel STs, designated ST305 and ST306. The most common ST was ST7 (72/106, 67.9%), which was the predominant type across all three hospitals, followed by ST3 (11/106, 10.4%). ST10 and ST203 were each represented by four strains (4/106, 3.8%), while ST22 and ST182 each comprised three strains (3/106, 2.8%). Single isolates were found for ST15, ST43, and ST45.

The antifungal susceptibility of the 106 C. glabrata isolates was also assessed. The susceptibility of C. glabrata to nine antifungal agents is shown in Table 2. A total of 23 (21.7%) C. glabrata isolates were resistant to fluconazole, with MICs ranging from 0.5 to ≥ 256 µg/mL. The non-wild-type (non-WT) rates of C. glabrata for voriconazole, itraconazole, and posaconazole were 23.6% (25/106), 7.5% (8/106), and 6.6% (7/106), respectively. Three isolates were found to be resistant to at least one of the echinocandins (2.8%). One of the three isolates was resistant to all the echinocandins, while the other two were resistant to caspofungin, with MICs of 1 µg/mL. Thus, resistance to echinocandins ranged from 0.94% (1/106) for micafungin or anidulafungin, and 2.8% (3/106) for caspofungin. The non-WT rate of C. glabrata for 5-flucytosine was 0.94%. All isolates showed WT MICs to AMB. In our study, we found that ST7 was the predominant type in fluconazole-resistant and voriconazole non-WT isolates (Table 3). All three strains showing resistance to echinocandins were ST7 (P > 0.05).

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Transcriptome analysis

In this study, three echinocandin-susceptible strains progressed to echinocandin-resistant strains after antifungal treatment lasting for more than 1 month. The transcriptomes of these echinocandin-resistant isolates (Q2-2, Q3-2, and Q4-2) and matched echinocandin-susceptible isolates (Q2-1, Q3-1, and Q4-1) were sequenced. The sequencing results for all six strains were mapped to the reference genome (CBS138). The results showed that the expression of three genes—CHS3, FKS1, and FKS2—was significantly upregulated in all three echinocandin-resistant isolates compared to that in the echinocandin-susceptible ones (P ≤ 0.05) (Table 4). Given that FKS mutations are well-documented in echinocandin resistance, we subsequently focused on the role of CHS3 in this process.

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Whole-genome sequencing

Whole-genome resequencing was performed to explore the mechanism underlying resistance to echinocandins in C. glabrata. Subsequently, we analyzed the SNPs, InDels, CNVs, and SVs in the highly expressed genes in both the drug-resistant and susceptible groups. The mapping rates of all samples ranged from 97.79 to 98.99%, and the average coverage depth of the reference genome ranged from 130.69 to 174.12×.

CHS3 is located on the chromosome with the accession number CP048122.1. Compared with the reference genome, the Q2-2 and Q3-2 isolates exhibited an A > G base substitution at position 246,852 of CHS3, resulting in a V91A amino acid change (Fig. 2A). Meanwhile, the Q4-2 strain contained a T > A base substitution at position 246,154 of the same gene, leading to an I324L amino acid change. The FKS1 gene is located on the chromosome with the accession number CP048124.1. Strain Q4-2 harbored a single non-synonymous Y3D mutation in the non-hotspot (non-HS) region of FKS1 (Fig. 2B). Lastly, the FKS2 gene is found on the chromosome with the accession number CP048128.1. Isolates Q3-2 and Q4-2 carried a S663P mutation in the FKS2 HS1 region and a S1382P mutation in the non-HS region of FKS2, respectively (Fig. 2C and D).

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CHS3 expression in C. glabrata

RT-PCR was used to evaluate the expression levels of CHS3 in the ATCC2001, Q2-2, ATCC2001-CHS3-OE, and Q2-2-CHS3Δ strains. The results showed that compared with ATCC2001, the CHS3 mRNA levels were significantly upregulated in the echinocandin-resistant strain (Q2-2) (P < 0.01) and the CHS3-OE strain, but were significantly downregulated in the CHS3Δ strain (Fig. 3A–C).

CFW staining and fluorescence microscopy for Chitin in the cell wall

The fluorescence intensity of chitin in the cell wall was assessed in the Q2-2, CHS3-OE, ATCC2001, and CHS3Δ strains of C. glabrata. The cell wall fluorescence intensity of the Q2-2 and CHS3-OE strains was 518 and 480, respectively, which was significantly higher than that of ATCC2001 (P < 0.01). In contrast, the cell wall fluorescence intensity of the CHS3Δ strain was significantly lower (P < 0.01) (Fig. 3D–H).

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Antifungal susceptibility testing in the Q2, CHS3-OE, CHS3Δ, and ATCC2001 C. glabrata strains

To further verify the role of CHS3 in echinocandin resistance in C. glabrata, drug susceptibility tests were carried out on the Q2, CHS3-OE, CHS3Δ, and ATCC2001 strains. The results showed that the MICs of echinocandins for the CHS3Δ strain were significantly reduced to ≤ 0.25 µg/mL, values that were more than 32-fold lower than those observed for the Q2 strains. Additionally, the MICs of echinocandins for the CHS3-OE strain were ≥ 4 µg/mL, significantly higher than those for ATCC2001 (Table 5).

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Discussion

Bloodstream infection caused by Candida is one of the most severe infectious diseases in clinical practice. Importantly, candidemia caused by C. glabrata is on the rise [18]. In recent years, C. glabrata has accounted for over 20% of all candidemia cases in certain regions of China [19, 20]. C. glabrata-related candidemia is particularly prevalent in eastern China. Furthermore, treating C. glabrata infections imposes a substantial economic burden on patients worldwide, due to the low susceptibility of this fungus to azoles and the notable side effects associated with treatment [21]. Consequently, there is an urgent need for more precise and effective therapeutic strategies for C. glabrata infection. In this study, we identified unique clinical and epidemiological characteristics of C. glabrata infections, and our findings provided evidence implicating CHS3 in echinocandin resistance.

Among the 103 patients, C. glabrata was most frequently detected in patients aged 51–80 years, a finding consistent with previous studies [22]. Data from the China Hospital Invasive Fungal Surveillance Net (CHIF-NET) also indicated a higher frequency of C. glabrata in older patients compared to the general average [23]. This increased susceptibility in older patients may be attributed to age-related weakening of the immune system, making them more vulnerable to pathogen infections. Notably, nearly 90% of C. glabrata infections occurred in patients who had received broad-spectrum antibiotics. Furthermore, patients on mechanical ventilation or parenteral nutrition also exhibited increased susceptibility to these infections. Additionally, the seroprevalence of C. glabrata was higher in patients from the Department of General Surgery and the ICU than in those of other departments, a finding that also aligns with data published by CHIF-NET [4]. This observation may be explained by the fact that C. glabrata primarily colonizes the skin surface, mucous membranes, and the gastrointestinal tract. Disruption of the skin and intestinal barriers due to surgical or invasive procedures can facilitate the entry of C. glabrata into the bloodstream, leading to infections. Awareness of these risk factors is crucial for clinicians. Avoiding non-essential invasive treatments and considering necessary preventive medication may help reduce C. glabrata transmission.

MLST, which relies on DNA sequencing, is widely used to monitor the distribution of pathogens across different geographical regions and periods [24,25,26]. In this study, we employed MLST for the genotyping of C. glabrata strains. Among the 106 isolates, ST7 accounted for the highest proportion. These findings are consistent with those reported by CHIF-NET for 2009–2014 [27, 28] and studies from Korea [29]. In contrast, ST59 and ST46 were the dominant STs in Iran and Kuwait [30, 31]. C. glabrata strains isolated from Japanese patients predominantly belonged to ST7 and ST30 [32]. However, even within Asia, significant variations in STs can occur with increasing geographical distance. In Europe, meanwhile, the isolates belonged mainly to ST3 and ST5, whereas those from the United States were primarily ST8 and ST18 [32]. These data highlight the existence of significant differences in C. glabrata STs across various countries, which has important implications for epidemiological investigations and strategies for the prevention of C. glabrata infections.

Drug sensitivity testing is crucial for guiding the treatment of C. glabrata infections, as these isolates are known to exhibit high azole resistance [11]. In this study, we found that over 20% of C. glabrata isolates were resistant to fluconazole and displayed non-WT to voriconazole. However, many isolates remained susceptible to itraconazole and posaconazole, consistent with CHIF-NET reports [33]. Interestingly, resistance rates vary by geographic region. Data from the Mayo Clinic showed that, apart from fluconazole, C. glabrata exhibits a very high level of resistance to azoles, with non-susceptible isolates exceeding 90% [34]. A study from France reported that 15.7% of C. glabrata isolates were resistant to fluconazole, whereas resistance to micafungin was low [35]. These differences in C. glabrata prevalence and resistance patterns may stem from various factors, including age and gender distribution, the genetic traits of the strains, clinical practices, and lifestyle variations across regions. We found no significant correlation between STs and drug resistance in our C. glabrata isolates, which contrasts with findings from a long-term, multi-center study in China [36]. Although all three echinocandin-resistant isolates in our study belonged to ST7, the chi-square test revealed no significant association, likely due to the small sample size. Mushi et al. showed that ST18 exhibited low sensitivity to fluconazole [37]. Therefore, further research with larger sample sizes and broader geographical representation is necessary to thoroughly evaluate the relationship between C. glabrata STs and antifungal drug sensitivity.

Under echinocandin pressure, Candida species can activate a variety of adaptive protection mechanisms to evade the killing effects of the drugs, a phenomenon known as drug tolerance [38]. In this study, three C. glabrata strains developed echinocandin resistance following prolonged antifungal treatment. The expression levels of the CHS3, FKS1, and FKS2 genes were found to be upregulated in these echinocandin-resistant strains. CHS3, which encodes a key enzyme involved in chitin synthesis in the cell wall, plays a crucial role in maintaining cell wall structural integrity [39, 40]. Within the cell wall, chitin, glucans, and glycoproteins are covalently cross-linked in a dynamic process [41]. In C. albicans, when β−1,3-glucan is damaged by echinocandins, the upregulation of CHS3 expression can strengthen the cell wall by increasing chitin content, thereby maintaining cell viability [42,43,44,45]. Furthermore, in both Saccharomyces cerevisiae and C. dubliniensis, CHS3 is uniquely required for the synthesis of the chitosan layer of the cell walls [46]. Calcineurin signaling-regulated increases in chitin content have also been shown to contribute to caspofungin tolerance in C. neoformans [47]. Another study reported that CHS3 was regulated by Pumilio-family RNA-binding protein 4 (Puf4), suggesting potential avenues for combination antifungal therapy [48]. Consequently, CHS3 is considered a potential therapeutic target for infections by C. albicans, C. neoformans, and other Candida species.

Numerous studies have established a link between FKS mutations and echinocandin resistance in C. glabrata [49, 50]. In this study, we identified three such mutations—S663P in the HS1 region of FKS2, S1382P in the non-HS region of FKS2, and Y3D in the non-HS region of FKS1. The S663P mutation was reported to be sufficient to confer echinocandin resistance in C. glabrata [51]. However, relatively little is known about the role of CHS3 in echinocandin resistance in this species. Sharma et al. [52] observed that CHS3 expression levels were higher in echinocandin-resistant C. glabrata isolates than in echinocandin-susceptible ones, which is consistent with our results. However, it remained unclear whether increased CHS3 expression contributes to reduced echinocandin sensitivity. To address this, we generated CHS3 deletion (CHS3Δ) and overexpression (CHS3-OE) strains using homologous recombination. These strains displayed significantly altered echinocandin drug sensitivity relative to their parental strains. As CFW binds to cell wall chitin, its fluorescence intensity accurately reflects the relative chitin content [53]. Our results suggest that CHS3 may indeed influence chitin content in the C. glabrata cell wall. We further screened for mutations in CHS3 among echinocandin-resistant isolates and identified two non-synonymous substitutions, namely, A970T and T272C, leading to the amino acid changes I324L and V91A, respectively. Cos et al. [54] showed that positions 993–995, consisting of arginine residues, along with the hydrophilic carboxy-terminal sequence (amino acids 956–989), are critical domains for the proper functioning of CHS3p in S. cerevisiae. Whether the I324L and V91A mutations contribute to CHS3-mediated echinocandin resistance in C. glabrata needs further investigation.

Overall, these findings suggest that CHS3 may play an important role in echinocandin resistance in C. glabrata. Notably, the expression of CHS3 is also regulated by the high osmolarity glycerol (HOG), protein kinase C (PKC), and Ca2+/calcineurin signaling pathways [55]. The deletion of factors within these pathways can affect the expression of CHS3 in C. albicans and increase its sensitivity to echinocandins [56], and a similar mechanism may be operative in C. glabrata. Based on these observations, we hypothesize that CHS3 promotes chitin synthesis in the C. glabrata cell wall, thereby counteracting the cell wall damage induced by echinocandins. Our study provides new insights into echinocandin resistance in C. glabrata, particularly adaptive resistance following prolonged drug administration.

This study had some limitations. First, the sample size was relatively small, and may not be fully representative of the whole Chinese population. Second, we did not verify the role of the A970T and T272C amino acid mutations in CHS3-mediated echinocandin resistance. Accordingly, whether these two non-synonymous mutations contribute to CHS3-mediated echinocandin resistance needs further investigation. Third, we did not explore the specific mechanisms and signaling pathways involved in the regulation of CHS3 expression. The mechanisms and pathways underlying CHS3-mediated echinocandin resistance should be examined in future studies.

Conclusions

Candidemia caused by C. glabrata is common in eastern China. C. glabrata exhibits high susceptibility to echinocandins but low susceptibility to azoles. Our findings suggest that CHS3 plays a crucial compensatory role in echinocandin resistance in C. glabrata, highlighting it as a promising target for future antifungal strategies.

Data availability

Sequence data that support the findings of this study have been deposited in the National Genomics Data Center with the project number CRA024066 (https://ngdc.cncb.ac.cn/gsa/browse/CRA024066).

Abbreviations

AMB:

Amphotericin B

CFW:

Calcofluor white

CHS3:

Chitin synthetase 3

CLSI:

Clinical and Laboratory Standards Institute

CNV:

Copy number variations

ECVs:

Epidemiological cutoff values

HOG:

High osmolarity glycerol

ICU:

Intensive care units

IGV:

Integrative Genomics Viewer

InDels:

Insertions and deletions

MALDI-TOF MS:

Matrix-assisted laser desorption ionization time-of-flight mass spectrometry

MICs:

The minimum inhibitory concentrations

MLST:

Multi-locus sequence typing

non-WT:

Non-wild-type

SDD:

Susceptibility dose-dependent

SNPs:

Single nucleotide polymorphisms

STs:

Sequence types

SV:

Structural variation

WT:

Wild-type

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