Abstract Carbohydrates are essential energy sources in the diets of humans and animals, yet the mechanisms underlying their utilization by gut fungi remain poorly understood. To address this gap, we employed Candida albicans—a prevalent gut fungal species in humans and pigs—as a model to investigate fungal carbohydrate utilization strategies. Using a multi-omics approach integrating transcriptomic and metabolomic analyses, we examined fungal growth dynamics, carbohydrate degradation patterns, and enzyme activity during in vitro fermentation. Our results revealed that C. albicans preferentially utilizes soluble polysaccharides, such as inulin and mannan-oligosaccharides (MOS), while exhibiting lower efficiency in degrading starch. Integrated transcriptomic and metabolomic analyses identified distinct metabolites and differentially expressed genes associated with carbohydrate metabolism, with strong correlations observed between carbohydrate-active enzymes (CAZymes) and specific metabolic intermediates. Notably, CAZyme expression was substrate-dependent: inulin specifically induced glycoside hydrolase family 15 (GH15, EC 3.2.1.3), which targets α-1,2-glycosidic linkages, whereas MOS upregulated a broader set of enzymes—including GH13_40 (EC 3.2.1.10), GH15, GH16_2 (EC 3.2.1-/2.4.1-) and GH17 (EC 3.2.1.58/2.4.1-) — that act on β-1,4-, α-1,6-, α-1,2-, and α-1,3-glycosidic bonds, mediating efficient extracellular hydrolysis of complex carbohydrates into absorbable monosaccharides. This study highlights the critical role of gut fungi in dietary carbohydrate utilization and provides novel insights into the mechanisms by which CAZymes mediate fungal carbohydrate metabolism. Graphical abstract Image, graphical abstract [35]Open in a new tab 1. Introduction Carbohydrates are the main energy source for humans and most monogastric animals, particularly pigs, providing 60–70 % of their total dietary energy ([36]Adebowale et al., 2019). Based on molecular size (degree of polymerization), carbohydrates are classified into three main groups: monosaccharides, oligosaccharides, and polysaccharides ([37]Amicucci, Nandita, and Lebrilla 2019). Starch and dietary fibers—particularly non-starch polysaccharides (NSP)—are abundant in animal feeds and human foods ([38]Lovegrove et al. 2017). Monogastric animals typically lack sufficient endogenous enzymes to degrade complex dietary polysaccharides ([39]Flint et al. 2012; [40]Oliphant and Allen-Vercoe 2019). Consequently, they rely on the complementary enzymatic activities of their microbial symbionts, specifically the diverse array of carbohydrate-active enzymes (CAZymes), to facilitate the breakdown and utilization of these polysaccharides ([41]Wardman et al. 2022). As model monogastric animals, pigs have been extensively studied for their gut microbiota's interactions with dietary carbohydrates (DCHO), including fiber, oligosaccharides, and starch ([42]Knudsen, Hedemann, and Lærke 2012; [43]Wang, Hu, et al. 2019; [44]Vasquez et al. 2022). Although research has focused on bacteria-DCHO interactions ([45]Li, Yang, et al. 2022; [46]Coker et al. 2021), the mycobiota (gut fungi comprising ∼0.1 % of GI microbiota) has been largely overlooked ([47]Nash et al. 2017). Recent studies have demonstrated that, despite their relatively low abundance compared to other microbes, the fungal component of the microbiota can significantly influence mammalian biology. While fungi are well known for their pathogenic potential, they also act as prominent colonizers in healthy hosts, underscoring their dualistic role in host–microbe interactions ([48]Hill and Round 2024). In contrast to the limited scrutiny afforded to gut fungi in monogastric animals, ruminant research has established anaerobic fungi (e.g. Neocallimastigomycota) as indispensable fibrolytic specialists, encoding > 300 CAZymes per genome that synergistically deconstruct lignocellulose matrices ([49]Lin et al. 2023; [50]Hagen et al. 2021; [51]Hartinger and Zebeli 2021). Therefore, monogastric mycobiota, particularly in pigs, requires greater research attention to elucidate its contributions to gut health and nutrition. Our previous work demonstrated that alterations in DCHO composition can trigger responses in the colonic fungal community of pigs. Among the tested carbohydrates, mannan-oligosaccharides (MOS) exerted the most pronounced impact, followed by starch and NSP. Notably, low-abundance fungi exhibited overlapping associations with both monosaccharides and CAZymes present in the digesta ([52]Luo et al. 2021). Collectively, these results demonstrate that gut fungi make essential contributions to porcine hindgut carbohydrate metabolism—a pivotal but understudied aspect of monogastric digestion. Understanding these fungal-mediated mechanisms could enable novel fiber utilization approaches in swine production, potentially improving feed efficiency. In this study, we investigated the response patterns and metabolic mechanisms underlying DCHO degradation by core gut fungi in pigs. To minimize the confounding influences of the complex intestinal environment, an in vitro cultivation strategy was employed. Building on our previous work ([53]Luo et al. 2021), we focused on Candida albicans, a prevalent fungal species in the porcine gut, and cultured it with a range of DCHOs as sole carbon sources under controlled conditions. Although C. albicans is a critical pathogen, its most common lifestyle is not pathogenicity but, rather, symptom-free commensal colonization ([54]Schille et al. 2025). To elucidate fermentation mechanisms and characterize CAZyme expression profiles, we adopted a multi-omics approach integrating metabolomics and transcriptomics. This study seeks to advance understanding of the nutritional and physiological roles of intestinal fungi and provide novel insights into their contributions to host carbohydrate metabolism. 2. Materials and methods 2.1. Strain and reagents The Candida albicans strain ATCC 10,231 (designated as Ca) was procured from Guangdong Microbial Culture Collection Center (Guangdong, China). The carbohydrate substrates used in this research included a diverse range: high-amylose maize starch (AM), containing 76.8 % amylose and 23.2 % amylopectin; high-amylopectin maize starch (AP), containing 2.8 % amylose and 97.2 % amylopectin; microcrystalline cellulose (MCC, CAS 9004–34–6, assay ≥ 99.0 %); inulin (In, CAS 9005–80–5, assay ≥ 90.0 %); wheat bran fiber (WF), containing 98 % insoluble dietary fiber (IDF); and mannan-oligosaccharides (MOS, assay ≥ 93.0 %). The sources of In and MCC were Sangon Biotech Co., Ltd (Shanghai, China), while the remaining substrates, AM, AP, MOS, and WF, were supplied by Quanwang Biotech Co., Ltd (Shanghai, China), Fuyang Biotech Co., Ltd (Dezhou, China), Solarbio Science & Technology Co., Ltd (Beijing, China), and Tubaite Science & Technology Co., Ltd (Chengdu, China), respectively. Furthermore, the culture medium essentials such as yeast extract and peptone were purchased from AOBOX Biotech Co., Ltd (Beijing, China). The content of total starch, amylose, and IDF in the substrates was determined using the amylose assay kit (K-AMYL) and total dietary fiber assay kit (K-TDFR-200A) from Megazyme International Ireland Ltd. (Wicklow, Ireland). 2.2. Experimental design and growth condition The C. albicans was cultivated in a modified YPD medium containing 1 % yeast extract, 1 % peptone, and supplemented with 1 % (w/v) of the specified carbohydrate substrate. A sterile medium devoid of the strain served as the blank control. The whole experimental design is shown in [55]Fig. 1. The growth dynamics of Ca were monitored by measuring the optical density at 600 nm (OD 600). Prior to inoculating the final cultures for growth experiments, the preculture was subcultured at least three consecutive times in the same medium to facilitate strain adaptation to the individual carbohydrate substrate. The 250 mL conical flasks were pre-warmed to 37 °C, and the Ca culture was initiated by inoculating 400 μl of the routinely subcultured Ca into 200 mL of the liquid medium using sterile syringes. The flasks were then incubated at 37 °C with agitation at 120 r/min for 96 h. Each treatment group consisted of three biological replicates. Culture samples were systematically collected at 0, 12, 24, 48, 60, 72, and 96 h. Morphological observations were performed on the samples, while the remaining portions were preserved at −80 °C for subsequent analysis. All downstream analyses (qPCR, saccharide quantification, metabolomics, RNA-seq) utilized aliquots from these same biological samples. qPCR assays included technical triplicates per biological sample. Fig. 1. [56]Fig 1 [57]Open in a new tab Study design for the whole experiment. 2.3. Scanning electron microscopy (SEM) SEM was utilized to investigate the microstructural and morphological features of C. albicans at the 96-hour time point across various substrate conditions. Preparation of samples for SEM observation involved centrifugation to collect fungal cells, which were subsequently fixed with 2.5 % (v/v) glutaraldehyde for 4 h. These cells underwent three rigorous washes with Phosphate Buffered Saline (PBS) to remove excess fixative, followed by dehydration through a graded series of alcohol concentrations (30 %, 50 %, 70 %, 80 %, 90 %, 95 %, and 100 %). Critical-point drying was performed to prevent surface distortion, and the cellular morphology was subsequently observed using a FEI Inspect F50 SEM (FEI, Hillsboro, USA), affording high-resolution imaging. 2.4. DNA extraction and quantitative PCR (qPCR) At each designated sampling time point, 1 mL of culture samples underwent centrifugation at 13,000 g and 4 °C for 10 min to separate the components. The supernatant was carefully discarded, and the resulting pellets were resuspended in 1 mL of PBS. Then the DNA was extracted from each pellet employing the cetyltrimethylammonium bromide (CTAB) method, as previously described ([58]Fujimura et al. 2014). For quantitative PCR (qPCR) assays targeting C. albicans, specific primers (F: 5′-TTTATCAACTTGTCACACCAGA-3′, R: 5′-ATCCCGCCTTACCACTACCG-3′) were utilized, and the reactions were performed on an ABI QuantStudio 5 platform (Applied Biosystems, USA), adhering to the protocols described before ([59]Guo et al. 2010). 2.5. Concentration of saccharides The concentrations of total polysaccharide and reducing sugar present in the supernatant of culture samples were accurately detected using commercial kits (Shanghai mlbio Biotechnology Co., Ltd and Beijing Solarbio Technology Co. Ltd) according to the manufacturer’s protocols. 2.6. Metabolomic analysis For metabolomic analysis, samples were meticulously selected from the soluble polysaccharide substrate groups, specifically the 96-h time points of the Ca-In and Ca-MOS groups. Additionally, pure In and MOS media served as the blank controls, labeled as the In and MOS groups, respectively. The samples were thawed at 4 °C and subsequently mixed with 1 mL of a methanol/acetonitrile/H[2]O solution (2:2:1, v/v/v) to facilitate extraction. Each sample from the same batch was equally pooled and injected at predetermined intervals to create the quality control (QC) samples. The resulting homogenate underwent low-temperature sonication for 30 min, followed by incubation on ice for 20 min to stabilize the mixture. Subsequently, the mixture was centrifuged at 14,000 g and 4 °C for 20 min, and the supernatant was dried using a vacuum centrifuge. The dried pellets were re-dissolved in 100 μl of an acetonitrile/H[2]O solvent (1:1, v/v), vortexed thoroughly, and centrifuged again at 14,000 g and 4 °C for 15 min. Finally, the clarified supernatant was injected into a liquid chromatography-mass spectrometry (LC-MS) system for analysis. The pre-processed samples were injected into an Ultra-High-Performance Liquid Chromatography (UHPLC) system (Agilent Technologies 1290) equipped with a 2.1 mm × 100 mm ACQUITY UPLC BEH 1.7 µm column (Waters, Ireland) and a TripleTOF 6600 mass spectrometer (AB Sciex). The chromatographic procedure was performed at a flow rate of 0.5 mL/min and a column temperature of 25 °C. In both ESI positive and negative modes, the mobile phases were A (25 mM ammonium acetate and 25 mM ammonium hydroxide in water) and B (acetonitrile). Separation was conducted under the follow gradient: 0∼0.5 min, 95 % B; 0.5∼7 min, 95 %∼65 % B; from 7∼8 min, 65 % B to 40 % B; 8∼9 min, 40 %∼40 % B; 9∼9.1 min, 40 %∼95 % B; 9.1∼12 min, 95 % B for equilibrating the systems. The injection volume was 2 μL. For metabolite identification, a rigorous approach was employed using public metabolite databases (HMDB, [60]https://www.hmdb.ca/) as the primary resource. Subsequently, orthogonal partial least squares-discriminant analysis (OPLS-DA) and hierarchical clustering analysis were leveraged to integrate and organize the obtained metabolite mass spectral peaks, enabling a comprehensive view of the metabolic landscape. To pinpoint significantly different metabolites, stringent criteria were applied, including a variable importance in projection (VIP) score threshold of ≥1.0, a fold change (FC) of either > 1.2 or < 0.833, and a P value from a t-test of < 0.05. 2.7. Transcriptomics analysis (RNA-Seq) For transcriptomic analysis, samples were selected from soluble substrate groups, specifically the 96-hour timepoints of the Ca-In and Ca-MOS treatments. Glucose was included as the control carbon source (Ca-C group), as it is a well-characterized, rapidly metabolized monosaccharide and a major degradation product of both inulin and MOS. Glucose is widely used in fungal transcriptomic studies to establish baseline metabolic activity and to differentiate substrate-specific transcriptional responses ([61]Li, Chroumpi, et al. 2022; [62]Li et al. 2023; [63]Blair et al. 2025). A no-carbon or inert control was not employed, as C. albicans exhibited severely impaired growth and minimal transcriptional activity under such conditions, which would confound the interpretation of substrate-responsive gene expression ([64]Nitsche et al. 2012). Total RNA was extracted from these culture samples using TRIzol® Reagent, adhering strictly to the manufacturer’s protocol (Magen, China). The quality of the RNA samples was rigorously assessed, first by measuring the A260/A280 absorbance ratio using a Nanodrop ND-2000 system (Thermo Scientific, USA), and subsequently by determining the RNA Integrity Number (RIN) on an Agilent Bioanalyzer 4150 system (Agilent Technologies, CA, USA). Only samples that met the predetermined quality standards were deemed suitable for library construction. Paired-end libraries were prepared using the ABclonal mRNA-seq Lib Prep Kit (ABclonal, China), following the manufacturer’s detailed instructions. Post-library preparation, PCR products were purified using the AMPure XP system to ensure their purity. The quality of the resulting libraries was then re-assessed on an Agilent Bioanalyzer 4150 system, prior to sequencing. Finally, the high-quality libraries were subjected to sequencing on an Illumina NovaSeq 6000 platform, generating 150 bp paired-end reads for comprehensive transcriptomic profiling. Initially, the raw data underwent processing with FastQC (Version 0.11.9) and Fastp (Version 0.23.2) software. This step involved refining the raw data by eliminating adapters, poly-N sequences, and low-quality reads to obtain clean reads. Subsequently, we acquired reference genome and gene model annotation files from the National Center for Biotechnology Information (NCBI) database. The reference genome was indexed, and the clean reads were aligned to it using HISAT2 software (Version 2.2.1). Gene counts were then quantified using FeatureCounts (Subread, Version 2.0.3). To normalize the expression levels, we utilized TPM (transcripts per kilobase of exon model per million mapped reads), taking into account both gene length and the number of mapped reads per gene. Differential expression analysis was conducted using the DESeq2 R package (Version 1.36.0). Differentially expressed genes (DEGs) were identified using a Benjamini–Hochberg adjusted P-value (Padj) < 0.05 and an absolute log₂ fold change (|log₂FC|) ≥ 1, to ensure the robust detection of statistically significant and biologically relevant changes. To gain insights into the functional enrichment of these DEGs and to highlight the differences in gene function between samples, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. For these analyses, we utilized the ClusterProfiler R package (Version 4.4.4). Significant enrichment was determined when the adjusted P value (Padj) was <0.05 for either GO or KEGG functions. Additionally, CAZymes were identified using the dbCAN2 platform ([65]https://bcb.unl.edu/dbCAN2/), which incorporates three tools, DIAMOND, HMMER, and dbCAN-sub. Annotations that were supported by at least two of these tools were retained to ensure robustness and reliability. 2.8. Statistical analysis For metabolomic data, a suite of multivariate statistical analyses, including PCA, OPLS-DA and KEGG analysis, were conducted using MetaboAnalyst 5.0 ([66]https://www.metaboanalyst.ca/). RNA-Seq data were analyzed using R. Network analysis was performed using Cytoscape (Version 3.9.1), with statistical significance defined as a nonparametric Spearman correlation coefficient of | R | > 0.8 and P< 0.05. Additional data were analyzed using SPSS 23.0 statistical software (SPSS Inc., Chicago, IL, USA). Normally distributed data among groups were compared using one-way analysis of variance (ANOVA) with Duncan’s multiple-range test. Differences were considered significant at P< 0.05, nonsignificant at P> 0.05, and indicative of a significant trend at 0.05< P< 0.1. 3. Results 3.1. Patterns of growth exhibited by C. albicans when utilizing various carbohydrate substrates To examine the growth characteristics of C. albicans on diverse carbohydrate substrates, we utilized an array of analytical methods, including qPCR, total polysaccharide content assessment, reducing sugar analysis, and scanning electron microscopy (SEM). Our growth assays demonstrated that all six carbohydrate substrates were effective in promoting the growth of C. albicans. A growth peak was observed at 24 h, followed by a subsequent decline. Notably, C. albicans exhibited a preference for utilizing inulin (In), mannan-oligosaccharides (MOS), and microcrystalline cellulose (MCC) compared to starch and wheat bran fiber (WF) ([67]Fig. 2A). We further conducted a comprehensive analysis of the changes in extracellular total polysaccharides and reducing sugars within the culture medium. The results indicated that the polysaccharide concentration initially rose, peaking at 36 h, and then decreased. Across all groups except the MOS group, the concentration of reducing sugars exhibited a fluctuating downward trend throughout the culture process ([68]Fig. 2B and [69]2C). These findings suggest that C. albicans possesses versatile capabilities for carbohydrate degradation. SEM analysis revealed a high abundance of fungal cells adhering to substrate particles, with some cells embedded within the particles ([70]Fig. 2D). Fig. 2. [71]Fig 2 [72]Open in a new tab Growth patterns and morphological analysis of C. albicans cultured with diverse carbohydrate substrates. (A) Fungal proliferation quantified by qPCR (log10 gene copies/mL) over 96 h. Data represent mean ± SEM (n = 3). Asterisks indicate significant differences between treatment groups at each time point: *P< 0.05, **P< 0.01, ***P< 0.001. (B) Total polysaccharide secretion (mg/mL) by C. albicans across substrates (mean ± SEM, n= 3). (C) Reducing sugar levels (mg/mL) in culture supernatants, reflecting substrate utilization (mean ± SEM, n= 3). (D) Scanning electron microscopy (SEM) images of C. albicans under each substrate. Notes: “In” means inulin, “WF” means wheat bran fiber, “AP” means high amylopectin corn starch, “AM” means high amylose corn starch, “MOS” means mannan-oligosaccharide, “MCC” means Microcrystalline Cellulose. 3.2. Metabolic profiles and KEGG pathway analysis of C. albicans cultivated on culture medium containing inulin and mannan-oligosaccharides The metabolome serves as a powerful tool to investigate the impact of various substrates on metabolite alterations during in vitro fermentation. This involves conducting qualitative and quantitative analyses of small molecule metabolites present in the fermentation broth. Growth patterns of C. albicans revealed a preference for utilizing soluble non-starch polysaccharides (NSP) such as inulin, as well as functional oligosaccharides like MOS. To delve deeper into the response and underlying mechanisms of C. albicans on these different substrates, we chose the inulin and MOS groups for comprehensive analysis employing both metabolomics and transcriptomics approaches. OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) was performed to evaluate the overall differences between the control and fermented carbohydrate samples (Figure S1). In both positive and negative ion modes, each group of samples was clearly distinguished, enabling a thorough bioinformatic analysis of the metabolome. Crucially, a significant separation was observed between the treatment groups (Ca-In and Ca-MOS) and the control group (pure In and MOS medium), suggesting substantial alterations in the metabolites present in the culture medium. In the Ca-In group, we identified 48 differential metabolites (DEMs) in positive ion mode, with 6 being up-regulated and 42 being down-regulated. In negative ion mode, 21 DEMs were detected, among which 9 were up-regulated and 12 were down-regulated ([73]Fig. 3A and [74]3B, Table S1). Compared to the control group, the primary DEMs in the Ca-In group encompassed 34 amino acid metabolism-related substances, 8 nucleotide metabolism-related substances, 7 carbohydrate metabolism-related substances, 4 bile acid metabolism-related substances, 2 vitamin metabolism-related substances, 1 sphingolipid metabolism-related substance and 13 other substances. Among the carbohydrate metabolism-related substances, sugars and their derivatives (including glycerol 3-phosphate, dihydroxyacetone, α-d-glucose, ribitol, citraconic acid, and glyceraldehyde) exhibited significant decreases (P< 0.05), with the exception of 2-dehydro-3-deoxy-d-gluconate, which showed a significant increase (FC = 1.92, P< 0.05). Fig. 3. [75]Fig 3 [76]Open in a new tab Differential metabolite profiles in C. albicans cultured with inulin and MOS carbon sources. (A) Ca-In vs. In (ESI^+), (B) Ca-In vs. In (ESI^-), (C) Ca-MOS vs. MOS (ESI^+), (D) Ca-MOS vs. MOS (ESI^-). In the Ca-MOS group, we identified 8 DEMs in positive ion mode, with 2 being up-regulated and 6 being down-regulated. In negative ion mode, 9 DEMs were detected, consisting of 4 up-regulated and 5 down-regulated ([77]Fig. 3C and [78]3D, Table S2). Compared to the control group, the DEMs in the Ca-MOS group encompassed 2 amino acid metabolism-related substances, 3 nucleotide metabolism-related substances, 5 bile acid metabolism-related substances and 7 other substances. Notably, carbohydrate metabolism-related substances were only detected in the melezitose (FC = 0.159, P= 0.06) and the citraconic acid (FC = 0.496, P= 0.055), both displaying a decreasing trend in their content. To gain a comprehensive understanding of the metabolic pathways, we conducted a KEGG enrichment analysis. The DEMs in the Ca-In group were predominantly associated with amino acid and carbohydrate metabolic pathways. Notably, pathways such as "glycolysis/gluconeogenesis", "galactose metabolism", "amino sugar and nucleotide sugar metabolism", "fructose and mannose metabolism", "butanoate metabolism" and "glyoxylate and dicarboxylate metabolism" were significantly enriched ([79]Fig. 4A depicts the top 25 metabolic pathways). Conversely, the KEGG enrichment analysis of the MOS group revealed that DEMs were primarily enriched in nucleotide, bile acid, and amino acid metabolism pathways ([80]Fig. 4B). Fig. 4. [81]Fig 4 [82]Open in a new tab KEGG pathway enrichment analysis of differential metabolites in C. albicans cultured with inulin (A) or MOS (B) as carbon substrates (top 25 enriched pathways). Bubble plots were generated using MetaboAnalyst 5.0. Dot properties: Color gradient reflects significance; red indicates higher significance (threshold: P < 0.05). Size corresponds to the enrichment ratio (proportion of metabolite hits per pathway). 3.3. Transcriptomic analysis unveiled potential mechanisms of C. albicans utilizing inulin and mannan-oligosaccharides To unravel the mechanisms underlying substrate utilization by C. albicans, an RNA-seq analysis was conducted. After rigorous quality filtering of the raw reads, an average of 62,610,389 clean reads per sample was obtained, with >91.2 % of the reads possessing a quality score of Q30. These high-quality reads were mapped to the reference genome with a relatively high efficiency, ranging from 77.46 % to 93.76 % across different samples (Table S3). DEGs were identified using stringent criteria: Padj < 0.05 and |log2 FC| ≥ 1. In comparison to the control groups, the Ca-In group exhibited 2235 DEGs, with 1007 genes up-regulated and 1128 genes down-regulated (Figure S2A). Similarly, the Ca-MOS group demonstrated 2276 DEGs when compared to the control group, including 1029 up-regulated genes and 1247 down-regulated genes (Figure S2C). Gene cluster analysis revealed distinct differences in gene expression patterns between the two groups, as visualized in the heatmap depicting the top 500 genes across different samples (Figure S2B and S2D). The functions of DEGs involved in substrate utilization by C. albicans were further investigated using GO and KEGG. According to the GO functional analysis, a significant enrichment of DEGs was observed in various categories (refer to Figure S3 for details). Specifically, within the biological process (BP) category, notable categories included "cell adhesion" (GO:0007155) and "carbohydrate metabolic process" (GO:0005975). In the molecular function (MF) category, a significant enrichment was observed in structural constituents of "oxidoreductase activity" (GO:0016491) and "RNA polymerase II transcription factor activity" (GO:0000981). Finally, in the cellular component (CC) category, the most prevalent categories were "extracellular region" (GO:0005576) and "integral component of membrane" (GO:0016021). KEGG pathway enrichment analysis for the Ca-In group demonstrated a significant enrichment of DEGs across 20 pathways. The most representative KEGG functional categories included “global and overview maps”, “carbohydrate metabolism”, “lipid metabolism”, “amino acid metabolism”, “cell growth and death” and “glycan biosynthesis and metabolism”. Notably, the “carbohydrate metabolism” subcategory stood out most prominently, encompassing 9 pathways such as “glyoxylate and dicarboxylate metabolism”, “citrate cycle (TCA cycle)”, “starch and sucrose metabolism”, “glycolysis/gluconeogenesis”, “galactose metabolism” and “fructose and mannose metabolism” ([83]Fig. 5A). In contrast, the Ca-MOS group exhibited significant enrichment of DEGs in 24 pathways compared to the control group. Within the "carbohydrate metabolism" subcategory, a total of 10 pathways were identified. Specifically, 23 DEGs were involved in "glycolysis/gluconeogenesis," and 12 DEGs were associated with "fructose and mannose metabolism." Additionally, other pathways such as "starch and sucrose metabolism" (with 22 DEGs), "TCA cycle" (with 18 DEGs), and "amino sugar and nucleotide sugar metabolism" (with 19 DEGs) were also notable ([84]Fig. 5B). Fig. 5. [85]Fig 5 [86]Open in a new tab KEGG pathway enrichment analysis differentially expressed genes in C. albicans cultured with inulin (A) or MOS (B) as the sole carbon source. The concentric circular plot displays: Outer circle: KEGG pathway categories (color-coded according to the legend on the right). Second circle: Bar lengths represent the number of background genes in each pathway, and bar colors indicate significance levels (-log10 Padj); darker colors denote higher significance. Third circle: Proportional bar graph showing the ratio of upregulated (yellow) and downregulated (green) genes in each pathway. Innermost circle: Rich factor values for each pathway; each grid line of the radial axis represents a value of 0.1. 3.4. CAZymes profiles of C. albicans across different carbohydrate substrates (inulin and mannan-oligosaccharides) The functional analysis revealed a significant enrichment of DEGs in several carbohydrate metabolism-related pathways. To delve deeper into the specific CAZymes involved in inulin utilization by C. albicans, we annotated these DEGs using the CAZyme database. In the Ca-In group, a total of 80 DEGs belonging to 48 CAZyme subfamilies were identified, compared to the control group ([87]Fig. 6A). Among these, 18 CAZyme genes were notably up-regulated, including 10 glycoside hydrolases (GHs), 3 glycosyltransferases (GTs), 1 carbohydrate esterase (CE), and 4 auxiliary activities (AAs). Conversely, 62 CAZyme genes were significantly down-regulated, comprising 24 GHs, 31 GTs, 1 CECE 1, and 6 AAs (Table S4). Additionally, taxonomic distribution analysis revealed a set of fungal-specific CAZymes in the Ca-In group, including GH13_40, GH5_9, GH5_49, GH16_2, GH16_18, GH132, CBM43+GH72, AA1, AA2, AA3, AA4, AA6, AA7, GT15, GT24, GT48, GT50, GT58, GT59, and GT91 (based on current CAZy database annotation; [88]https://www.cazy.org/, accessed June 10, 2025; Table S5). To further investigate the potential roles of these CAZymes in metabolic adaptation, we mapped the DEGs of CAZyme-related genes onto key carbon metabolism pathways. The CAZyme expression profiles exhibited a substrate-dependent pattern. Inulin treatment specifically induced the expression of GH15 (EC 3.2.1.3), an enzyme that cleaves α-1,2-glycosidic linkages during inulin degradation into soluble monosaccharides (glucose and fructose). These hydrolytic products are subsequently channeled into sugar metabolism and central carbon metabolism, including “starch and sucrose metabolism”, “fructose and mannose metabolism”, “galactose metabolism”, “TCA cycle”, and “glyoxylate cycle”, thereby providing substrates for cellular energy production and biosynthetic processes ([89]Fig. 6C). Fig. 6. [90]Fig 6 [91]Open in a new tab CAZyme annotation profile and related Carbohydrate metabolic networks of C. albicans cultured with inulin or MOS substrates. (A-B) Heatmap of differentially expressed CAZyme genes in C. albicans (Ca-In vs. In) and (Ca-MOS vs. MOS). Color scale represents DEGs fold-change values (red: upregulation; blue: downregulation). (C-D) Carbohydrate metabolic networks for inulin (C) and MOS (D). The metabolic pathways include glycolysis/gluconeogenesis, TCA cycle, glyoxylate cycle, pyruvate metabolism, propanoate metabolism, butanoate metabolism, and related sugar metabolism (e.g., starch/sucrose, fructose/mannose, galactose). Enzyme labels: EC numbers are provided for each reaction; upregulated genes highlighted in red, downregulated genes in blue. Compound names in green indicate significantly decreased metabolites (DEMs). In the Ca-MOS group, a total of 78 DEGs belonging to 49 CAZyme subfamilies were identified as significantly altered in expression compared to the control group ([92]Fig. 6B). Among these, 13 CAZyme genes were significantly up-regulated (including 6 GHs, 3 GTs, and 4 AAs), while 65 CAZyme genes were down-regulated (including 26 GHs, 33 GTs, 1 CECE 1, and 5 AAs) (Table S6). Consistent with the Ca-In group, a variety of fungal-specific CAZymes were also identified in the Ca-MOS group, such as GH13_40, GH5_9, GH5_49, GH16_2, GH16_18, GH132, CBM43+GH72, AA1, AA2, AA6, AA7, GT15, GT48, GT50, and GT91. C. albicans cultured with MOS exhibited a broader CAZyme activation profile ([93]Fig. 6D). MOS treatment upregulated a diverse array of glycoside hydrolases, including GH13_40 (EC 3.2.1.10), GH15 (EC 3.2.1.3), GH16_2 (EC 3.2.1-/2.4.1-), and GH17 (EC 3.2.1.58/2.4.1-), which collectively target β-1,4-, α-1,6-, α-1,2-, and α-1,3-glycosidic bonds. These enzymes facilitate the efficient extracellular breakdown of complex oligosaccharides into absorbable monosaccharides, including glucose, galactose, mannose, and N-acetylglucosamine. The resulting hydrolytic products are subsequently funneled into sugar metabolism, central carbon metabolism, and amino sugar and nucleotide sugar metabolism, supporting energy production and anabolic processes. 3.5. Comparative metabolomics and transcriptomics analyses revealed the distinctive metabolic features of C. albicans when grown on inulin and mannan-oligosaccharides To elucidate the pivotal pathways and targets involved in the metabolism of inulin and MOS by C. albicans, we conducted an integrated analysis of both the metabolome and transcriptome. In comparing the Ca-In group to the control, the KEGG enrichment analysis pinpointed 40 common metabolic pathways, predominantly related to carbohydrate and amino acid metabolism (Figure S4A). Among these, seven pathways specifically linked to carbohydrate metabolism were identified, encompassing “amino sugar and nucleotide sugar metabolism”, “butanoate metabolism”, “glyoxylate and dicarboxylate metabolism”, “fructose and mannose metabolism”, “glycolysis/gluconeogenesis”, “pentose phosphate pathway”, and “galactose metabolism” ([94]Fig. 7A). Fig. 7. [95]Fig 7 [96]Open in a new tab Integrated metabolomic and transcriptomic profiling of C. albicans cultured with inulin (A-B) or MOS (C-D) substrates. (A) KEGG pathway enrichment analysis (Ca-In group) showing coordinated changes in DEGs (circles) and DEMs (triangles). (B) Correlation network of CAZymes and carbohydrate-related metabolites (Ca-In group), edges denote significant correlations (red: positive, blue: negative, with |R| > 0.8 and P < 0.05). (C) KEGG pathway enrichment analysis in Ca-MOS group. (D) Correlation heatmap of CAZyme-metabolite interactions in Ca-MOS group. Color gradient represents Spearman correlation coefficients (red: positive, blue: negative). Asterisks (*) denote statistically significant correlations (|R| > 0.8, P < 0.05). With a focus on carbohydrate metabolism, we delved into a network analysis to decipher the interplay between carbohydrate-related metabolites and CAZymes. Notably, most CAZymes exhibited a positive correlation with metabolites such as glycerol 3-phosphate, dihydroxyacetone, α-d-glucose, ribitol, glyceraldehyde, and citraconic acid (|R| > 0.8, P< 0.05). Remarkably, GT24, AA2, and GH15 were exclusively associated with 2-dehydro-3-deoxy-d-gluconate, while GH5_9 and GT8 uniquely correlated positively with ribitol. Additionally, GT1, GH17, CE4, and CE1 were specifically linked to citraconic acid ([97]Fig. 7B). In contrast, when comparing the Ca-MOS group to the control, we identified 10 shared KEGG pathways (Figure S4B), primarily involving amino acid metabolism, nucleotide metabolism, and the biosynthesis of secondary metabolites ([98]Fig. 7C). It is noteworthy that only two carbohydrate-related metabolites exhibited differential trends (0.05 < P< 0.1). A correlation hierarchical clustering analysis between these metabolites and CAZymes revealed that GT34, GT15, and GT58 were positively correlated with melezitose (|R| > 0.8, P< 0.05), while GH15 exhibited a negative correlation. Furthermore, GT15, GT58, GT50, GH63, AA4, GT24, and GT76 were positively correlated with citraconic acid, whereas AA2, AA3, and GT1 showed a significant negative correlation ([99]Fig. 7D). 4. Discussion Carbohydrates are the primary energy source for both humans and animals ([100]Payling et al. 2020). Monogastric animals possess a limited repertoire of endogenous enzymes for degrading structurally diverse dietary carbohydrates ([101]Oliphant and Allen-Vercoe 2019). As a result, the digestion of complex polysaccharides (e.g., cellulose and starch) largely depends on CAZymes produced by the intestinal microbiota ([102]Berlemont and Martiny 2016). Microbial symbionts play a central role in carbohydrate degradation, through microbial enzymatic activity, complex carbohydrates are hydrolyzed into short-chain oligosaccharides and monosaccharides that can be absorbed and metabolized by the host. Therefore, in monogastric animals, efficient carbohydrate digestion is predominantly driven by the gut microbiota and their CAZyme repertoire (La [103]Rosa et al. 2022; [104]Sheridan et al. 2016). Among the vast array of genes identified in the human gut microbiome, those encoding CAZymes are particularly noteworthy due to their essential role in the digestion of structurally diverse dietary polysaccharides ([105]Berlemont and Martiny 2015). Notably, members of the phylum Bacteroidetes harbor a greater number and diversity of CAZyme genes than other microbial taxa, enabling them to target a wide spectrum of polysaccharides commonly found in the diet ([106]Martens et al. 2009; [107]Cerqueira et al. 2020). In ruminants, rumen fungi contribute an additional and distinct CAZyme reservoir, encoding highly potent enzymes specialized in the breakdown of recalcitrant plant biomass ([108]Hagen et al. 2021). These fungal CAZymes play a pivotal role in facilitating the digestion of fibrous roughage, underscoring their functional significance in ruminant nutrition ([109]Lin et al. 2023; [110]Hagen et al. 2021; [111]Hartinger and Zebeli 2021). In contrast, the nutritional and physiological roles of gut fungi in monogastric animals remain largely unexplored. C. albicans, the model strain used in this study, colonizes the gut in 40–60 % of healthy individuals and is generally regarded as a commensal organism. Emerging evidence suggests that its presence may contribute to various host physiological processes ([112]Shao et al. 2022). In vitro cultivation with six different carbohydrates as sole carbon sources revealed notable differences in substrate utilization efficiency. The fermentability of dietary fibers is known to depend on their solubility, molecular structure, and glycosidic linkage types ([113]Hamaker and Tuncil 2014; [114]Tao et al. 2019; [115]Guan, Yu, and Feng 2021). Soluble oligosaccharides are generally fermented rapidly, followed by soluble polysaccharides like inulin and arabinoxylan ([116]Bai et al. 2021), whereas insoluble fibers such as resistant starch and wheat bran degrade more slowly ([117]Wang, Wichienchot, et al. 2019; [118]Meng et al. 2022). Our results showed that C. albicans efficiently utilized inulin, MOS, and MCC, but exhibited limited degradation of starch and wheat bran. SEM analysis confirmed fungal adherence to substrates. Interestingly, its relatively high utilization of crystalline MCC suggests that gut fungi may possess greater capacity to access ordered fiber structures than previously reported in bacterial fermentation studies. The observed fluctuations in total polysaccharide and reducing sugar concentrations reflect the dynamic processes of carbohydrate degradation and sugar metabolism during fungal fermentation. In line with previous studies ([119]Gao et al. 2024; [120]Wardman et al. 2022), we observed an initial increase in reducing sugars, followed by a gradual decline. This pattern is typically driven by active extracellular hydrolysis of polysaccharides by CAZyme-rich primary degraders, with the released sugars either taken up for intracellular metabolism or accumulating temporarily in the medium. As substrate availability decreases, polysaccharide degradation slows, and microbes shift toward consumption of the accumulated sugars ([121]Guo et al. 2021). Interestingly, substrate-specific differences were also evident. In the MCC group, fungal growth remained robust despite only minor reductions in total polysaccharide levels. This may be attributed to the structural heterogeneity of microcrystalline cellulose, where CAZymes preferentially hydrolyze amorphous regions, releasing glucose monomers while the crystalline domains remain largely resistant ([122]Lupidi et al. 2023; [123]Tachioka et al. 2024). In contrast, in the MOS group, reducing sugar levels remained relatively stable even during sustained fungal growth. This may reflect a dynamic balance between extracellular oligosaccharide hydrolysis and intracellular uptake, potentially mediated by substrate-induced CAZymes and specific sugar transporters ([124]Xu et al. 2025). But we acknowledge the limitations of the phenol-sulfuric acid method in accurately quantifying specific sugars, and that reducing sugar measurements alone do not provide a full carbon mass balance. Future studies should combine targeted metabolomics with comprehensive carbon recovery analysis to improve resolution and rigor Based on qPCR data and sugar concentration measurements, C. albicans displayed a clear preference for soluble non-starch polysaccharides (NSPs), such as inulin, and functional oligosaccharides like MOS. Metabolomic analysis further revealed that in the Ca-In group, approximately 70.8 % of the 34 identified differential metabolites were associated with amino acid metabolism. This enrichment may largely reflect the influence of complex nitrogen sources (peptone) in the culture medium, which can broadly activate amino acid metabolic pathways and potentially mask substrate-specific metabolic signals ([125]Tong et al. 2022). To better resolve carbon-source-driven metabolic changes, future studies should consider using chemically defined or minimal media to reduce background interference. Notably, purine metabolism was also enriched in the Ca-In group. Purines are vital for DNA/RNA synthesis, energy metabolism, and signal transduction in organisms, and they play a crucial role in fungal growth and development ([126]Sun et al. 2021). Inulin is a linear fructan composed of β−1,2-linked fructose units terminated by an α−1,2-linked glucose moiety ([127]Tawfick et al. 2022). Transcriptomic and pathway analysis suggest that C. albicans degrades inulin primarily via the upregulation of the GH15 gene (EC 3.2.1.3), which targets α−1,2-glycosidic bonds. Previous metagenomic studies of pig gut microbiota have identified GH13, GH15, GH16, and GH32 as key CAZyme families enriched in Firmicutes and Bacteroidetes, playing a major role in the initial depolymerization of dietary polysaccharides ([128]Wang, Hu, et al. 2019; [129]Xu et al. 2021; [130]Holman et al. 2022). Our findings suggest that C. albicans encodes and modulates these CAZyme families, highlighting its potential role in the degradation of dietary polysaccharides such as inulin. When C. albicans was cultured in MOS medium, no significant changes in carbohydrate metabolites were detected in the culture supernatant. As metabolic profiling was performed at a single late time point (96 h), the reults likely reflect cumulative or stationary-phase metabolic states rather than early adaptive responses or active substrate degradation. Despite this, transcriptomic analysis revealed significant enrichment of KEGG pathways related to carbohydrate metabolism. In particular, 13 CAZyme genes were upregulated, many of which are associated with the breakdown of complex polysaccharides and oligosaccharides such as cellulose, starch, and β-glucan ([131]Ravn et al. 2021; [132]Panwar, Shubhashini, and Kapoor 2023). Given that MOS primarily comprises mannans linked with glucose or galactose residues via β-1,4- and α-1,6-glycosidic bonds (with branching through α-1,2- and α-1,3-linkages ([133]Narisetty et al. 2022), C. albicans appears to adapt by upregulating key enzymes (e.g., GH13_40, GH15, GH16_2, and GH17) targeting these specific glycosidic bonds. To better understand the temporal dynamics of fungal substrate utilization and adaptation, future studies should incorporate time-resolved transcriptomic and metabolomic analyses. In addition, for metabolic profiling, including heat-inactivated fungal controls will be essential to distinguish active metabolic responses from potential abiotic changes in the medium. Notably, using CAZypedia for functional annotation, our study identified several CAZyme families that appear to be specifically encoded by fungi, including GH13_40, GH5_9, GH5_49, GH16_2, GH16_18, GH132, CBM43+GH72, AA1, AA4, AA6, AA7, GT15, GT24, GT48, GT50, GT58, GT59, and GT91. Among them, GH72 is an endo-β−1,3-glucanase involved in the elongation and remodeling of fungal cell wall β−1,3-glucans, and its full-length form often includes a CBM43 module at the C-terminus ([134]Aimanianda et al. 2017). We also found that, regardless of whether inulin or MOS was used as the sole carbon source, certain chitinase (such as GH18) was highly expressed. Chitinases play a role exogenous chitin or chitosan decomposition but also in fungal cell wall degradation and morphogenesis ([135]Hartl, Zach, and Seidl-Seiboth 2012). These findings suggest that C. albicans possesses a distinctive CAZyme repertoire, enabling flexible responses to varying carbon sources. Such enzymatic capabilities may confer a competitive ecological advantage during dietary shifts or microbial dysbiosis. By modulating nutrient competition and cross-feeding interactions, these metabolic traits may influence the structure and function of the gut microbial community. Howerver, dbCAN2-based CAZyme annotations predict metabolic potential, the lack of experimental validation (e.g., gene knockout or enzyme inhibition) precludes definitive functional characterization. Moreover, although our in vitro data suggest that C. albicans may participate in dietary carbohydrate degradation under anaerobic conditions, its actual functional role within the gut ecosystem remains to be confirmed. Future in vivo studies are essential to clarify its ecological relevance and mechanistic contributions in host-associated contexts. In summary, our study focused on the gut fungus C. albicans, a resident fungal species in the intestinal tract of a pig, revealing that inulin and MOS are the preferred soluble carbohydrate substrates for this fungus in vitro. We conducted a multi-omics analysis to investigate variations in metabolites and CAZyme expression profiles. Differential metabolites and genes were enriched in multiple carbohydrate metabolism pathways, and CAZymes exhibited significant correlations with these metabolites. C. albicans demonstrates distinct patterns in carbohydrate catabolism, primarily through the regulation of CAZyme gene expression. Various fungal-encoded specific CAZymes involved in the degradation of fermentable substrates (i.e., polysaccharides) and the biosynthesis of secondary metabolites are likely to play a crucial role in the ecological adaptation of fungi to the gut environment. While our in vitro system does not fully replicate host conditions, the observed metabolic flexibility under dietary polysaccharides provides insight into potential mechanisms of fungal persistence in the gut. These results lay the groundwork for future studies employing gut-mimicking or in vivo systems to validate fungal adaptation strategies in host environments Author contributions JL, DC, and YL conceived and designed the experiments. DC and YL administered the project. JL performed the experiments and collected and analyzed the data. JL and YL wrote and revised the manuscript. BY, JH, HW and QW contributed valuable advice on the data analysis and manuscript. All authors reviewed the manuscript and agreed to the published version of the manuscript. Funding This work was supported by the National Natural Science Foundation of China (NSFC; grant numbers 31,730,091 and 32,072,743). Declaration of competing interest All authors declare that they have no competing interests. Footnotes Supplementary material associated with this article can be found, in the online version, at [136]doi:10.1016/j.crmicr.2025.100451. Appendix. Supplementary materials [137]mmc1.docx^ (397.6KB, docx) [138]mmc2.xlsx^ (17.2KB, xlsx) [139]mmc3.xlsx^ (16.6MB, xlsx) [140]mmc4.xlsx^ (16.5KB, xlsx) Data availability The RNA sequencing raw data have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA1141727. Metabolomics raw data are available through the China National Center for Bioinformation (CNCB-NGDC) under BioProject accession PRJCA041866. All supporting data are included in the manuscript and supplementary materials References