Candida albicans PPG1, a serine/threonine phosphatase, plays a vital role in central carbon metabolisms under filament-inducing conditions: A multi-omics approach

Abstract

Candida albicans is the leading cause of life-threatening bloodstream candidiasis, especially among immunocompromised patients. The reversible morphological transition from yeast to hyphal filaments in response to host environmental cues facilitates C. albicans tissue invasion, immune evasion, and dissemination. Hence, it is widely considered that filamentation represents one of the major virulence properties in C. albicans. We have previously characterized Ppg1, a PP2A-type protein phosphatase that controls filament extension and virulence in C. albicans. This study conducted RNA sequencing analysis of samples obtained from C. albicans wild type and ppg1Δ/Δ strains grown under filament-inducing conditions. Overall, ppg1Δ/Δ strain showed 1448 upregulated and 710 downregulated genes, representing approximately one-third of the entire annotated C. albicans genome. Transcriptomic analysis identified significant downregulation of well-characterized genes linked to filamentation and virulence, such as ALS3, HWP1, ECE1, and RBT1. Expression analysis showed that essential genes involved in C. albicans central carbon metabolisms, including GDH3, GPD1, GPD2, RHR2, INO1, AAH1, and MET14 were among the top upregulated genes. Subsequent metabolomics analysis of C. albicans ppg1Δ/Δ strain revealed a negative enrichment of metabolites with carboxylic acid substituents and a positive enrichment of metabolites with pyranose substituents. Altogether, Ppg1 in vitro analysis revealed a link between metabolites substituents and filament formation controlled by a phosphatase to regulate morphogenesis and virulence.

About the Authors:

Mohammad Tahseen A. L. Bataineh

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected] (MTALB); [email protected] (MH)

Affiliations College of Medicine, University of Sharjah, Sharjah, UAE, Research Institute for Medical & Health Sciences at University of Sharjah, Sharjah, UAE, Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, UAE, Department of Genetics and Molecular Biology, College Of Medicine And Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, UAE

ORCID logo https://orcid.org/0000-0002-8855-5320

Nelson Cruz Soares

Roles Conceptualization, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Writing – original draft, Writing – review & editing

Affiliations Research Institute for Medical & Health Sciences at University of Sharjah, Sharjah, UAE, Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah, UAE

ORCID logo https://orcid.org/0000-0003-2331-8532

Mohammad Harb Semreen

Roles Data curation, Formal analysis, Methodology, Resources, Software, Supervision, Writing – original draft, Writing – review & editing

Affiliations Research Institute for Medical & Health Sciences at University of Sharjah, Sharjah, UAE, Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah, UAE

Stefano Cacciatore

Roles Data curation, Formal analysis, Resources, Software, Visualization, Writing – original draft, Writing – review & editing

Affiliations Cancer Genomics Group, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa, Institute for Reproductive and Developmental Biology, Imperial College, London, United Kingdom

Nihar Ranjan Dash

Roles Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Affiliation: College of Medicine, University of Sharjah, Sharjah, UAE

Mohamad Hamad

Roles Formal analysis, Software, Supervision, Writing – original draft, Writing – review & editing

Affiliations Research Institute for Medical & Health Sciences at University of Sharjah, Sharjah, UAE, Department of Medical Laboratory Sciences, College of Health Sciences, University of Sharjah, Sharjah, UAE

Muath Khairi Mousa

Roles Data curation, Formal analysis, Methodology, Resources, Software, Writing – original draft, Writing – review & editing

Affiliation: Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah, UAE

Jasmin Shafarin Abdul Salam

Roles Data curation, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing

Affiliation: Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah, UAE

Mutaz F. Al Gharaibeh

Roles Software, Visualization, Writing – original draft, Writing – review & editing

Affiliation: College of Medicine, University of Sharjah, Sharjah, UAE

Luiz F. Zerbini

Roles Resources, Software, Supervision, Writing – original draft, Writing – review & editing

Affiliation: Cancer Genomics Group, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa

Mawieh Hamad

Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Software, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected] (MTALB); [email protected] (MH)

Affiliations Research Institute for Medical & Health Sciences at University of Sharjah, Sharjah, UAE, Department of Medical Laboratory Sciences, College of Health Sciences, University of Sharjah, Sharjah, UAE

ORCID logo https://orcid.org/0000-0002-6769-1091

Introduction

Candida albicans is the most common opportunistic fungal pathogen in humans. It causes mucosal and systemic infections and is of medical significance due to its ability to cause hospital-acquired bloodstream infections with high mortality [1,2]. C. albicans is part of the normal flora of the oral cavity and the gastrointestinal and genitourinary tracts in healthy individuals. Immunocompromised hosts, including cancer patients on chemotherapy, organ transplant recipients, and patients with indwelling catheters, often develop disseminated candidiasis, a systemic form of the disease with close to 40% mortality [3–5]. Antifungal therapies available to treat systemic candidiasis are limited, and current therapies have adverse side effects [6,7].

C. albicans is a successful commensal with no known reservoirs outside the mammalian host and possesses multiple virulence properties that cause disease. A significant virulence property is the ability to undergo a dimorphic shift from single oval-shaped cells (yeast) into elongated cells attached end-to-end (pseudohyphal and hyphal filaments) in response to host environmental conditions [8–10]. Hyphal filaments are associated with virulence and virulence-related properties, including tissue invasion, lysis of macrophages and neutrophils, and breaching of endothelial cells [11–13]. Numerous genes expressed during the reversible morphogenic switch encode proteins that play crucial roles in virulence, such as secreted aspartic proteases (SAPs), which facilitate tissue damage, and adhesins, important for attachment of C. albicans to host surfaces [14,15]. Furthermore, transition to the hyphal form endows C. albicans with the ability to evade innate immunity [16]. Various host environmental stimuli induce C. albicans yeast-hyphal transition through a coordinated expression of transcriptional regulators influencing multiple signal transduction pathways, including the MAP kinase pathway and the Ras-cAMP-Protein Kinase A (PKA) pathway, among others [17]. During morphological switching, the activity of signaling pathway components is controlled by kinases and fine-tuned by phosphatases [18]. For example, C. albicans can survive harsh environmental conditions within the host owing to their ability to produce rapid and robust stress responses. Stress-activated protein kinase (SAPK) pathways tightly regulate these stress responses [19]. However, the contribution of phosphatases in fungal stress responses remains ambiguous.

Metabolic adaptation is vital for dimorphic switching in pathogenic yeast. During yeast to hyphal morphologic transition, C. albicans cells grown under hyphae-inducing conditions showed an overall downregulation of cellular metabolism and significant downregulation of carbon metabolism metabolic pathways [20]. Similarly, another metabolomic study performed with azole sensitive and resistant C. albicans strains identified a significant change in metabolic processes such as amino acid metabolism, tricarboxylic acid cycle, and phospholipid metabolism during the development of resistance to azole drugs [21]. Previous studies have shown that Candida strains deficient for PPG1, which encodes a putative serine/threonine PP2A phosphatase, remain locked in yeast or short germ tube forms and show reduced virulence mouse model of systemic candidiasis [22]. PP2A phosphatases help maintain cell wall integrity, actin cytoskeleton organization, auxin signaling in plant cells, polar movement, and mitophagy [23–26]. Although PPG1 was previously reported to plays a vital role in C. albicans filament extension and virulence, its exact role in the multitude of transcriptional networks and signaling pathways that regulate fungal morphogenesis remains unclear. To gain more insight into the poorly understood role of phosphatases in controlling C. albicans filamentation and virulence, we performed a sequence-based analysis of RNA samples obtained from wild-type and ppg1Δ/Δ C. albicans cells grown under filament-inducing conditions as means of defining PPG1-related transcriptomic signatures. To complement our transcriptomics findings, we performed a detailed analysis of the metabolomic profiles of wild-type and ppg1Δ/Δ C. albicans to understand further the bearing of PPG1 on crucial metabolic pathways under different growth conditions.

Materials and methods

Strains, media, and culture conditions

Wild-type C. albicans (DK318) and ppg1Δ/Δ (MAY34) strains were used throughout this study as described previously [27]. Yeast extract-peptone-dextrose (YEPD) medium at 30°C was used as the standard non-filament-inducing growth condition for all strains. Liquid serum and temperature induction experiments were performed by growing strains overnight in YEPD medium at 30°C to an optical density at 600 nm (OD600) of ∼4.0 and diluting 1:10 into 50 ml of pre-warmed YEPD medium plus 10% serum at 37°C as described previously [27]. Aliquots of cells harvested at specific post-induction time points 3 and 5 hours for RNA isolation. We selected 3 and 5 hours-time points as these times points show the most prominent phenotypic (filamentation) difference between C. albicans wild-type and ppg1Δ/Δ strains as shown before [28].

RNA isolation, purification, and sequencing

RNA extraction was performed using RNeasy Micro Kit (Qiagen GmbH, Germany), following the manufacturer’s instructions. Three biological replicates were obtained for each condition (W.T. and mutant). RNA sequencing was performed at BGI Group, Shenzhen, China. RNA was extracted from 8 samples belonging to two different strains, DK318 and MAY34 are described in Table 1.

[Figure omitted. See PDF.]

Table 1. Data description.

https://doi.org/10.1371/journal.pone.0259588.t001

RNA sequencing and filtering

RNA obtained from 8 samples (Table 1) was sequenced using the BGISEQ-500 platform (Shenzhen, China), generating on average about 23.93 million reads per sample. The average mapping ratio with reference genomes was 96.92%, and the average gene mapping ratio was 82.77%; a total of 6,113 genes were detected. Sequence reads containing low-quality, adaptor-polluted and/or unknown high base (N) content were excluded from any further analysis. Retained sequence reads were further filtered using internal software SOAPnuke to produce a set of "clean reads" that was stored in FASTQ format as per each sample. Composition filtering statistics of raw data and quality metrics of clean reads are shown in S1 Table in S1 File.

Gene mapping analysis

Clean filtered sequence reads were mapped to the reference genome using HISAT (hierarchical indexing for spliced alignment of transcripts) to perform the genome mapping step. Reads were mapped to the C. albicans strain SC5314 reference genome (assembly 21) (http://www.candidagenome.org). On average, 96.92% reads were mapped, and the uniformity of the mapping result for each sample suggested that the samples were comparable.

Gene expression analysis

Clean reads were mapped to reference transcripts using Bowtie2 [29] v2.2.5, and gene expression levels in each sample were calculated using RSEM [30] v1.2.12; mapping details are shown in S2 Table in S1 File.

Metabolite extraction and derivatization

The exact number of cells was used for each sample to avoid the effect of variable cell numbers. A volume of 300μL of the extraction solvent (acetonitrile: water, 1:1 v/v) was added to the cell pellets (2 million cells per pellet). The cells were then vortexed for 2 min to ensure the quantitative extraction of the metabolites and then stored in ice for one h, during which the samples were vortexed every 15 min. The insoluble cell matrices were then centrifuged (13,000 rpm, 10 min, −4°C).

The supernatants were collected and transferred to G.C. vials for drying using EZ-2 Plus (GeneVac-Ipswich, UK) at 37 ± 1°C. Polar metabolites such as amino acids and saccharides cannot be analyzed directly by G.C. due to their low volatility. Hence, it was necessary to derivatize them before the G.C.–M.S. analysis. The dry samples were dissolved in 25μL of 20 mg/mL methoxyamine hydrochloride in pyridine, followed by vortexing for 2 min, and stored for at least six h at 25°C before the silylation step. Next, 25 μL of N-Methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) + 1% Trimethylchlorosilane (TMCS) were added, which were then dissolved in 100 μL of pyridine and vortexed for 2 min. For complete derivatization, the samples were incubated at 50°C for 30 min and then transferred to 200μL micro-inserts and analyzed by G.C.–M.S.

Gas chromatography-mass spectrometric analysis of the samples

G.C.–M.S. analysis was performed using a QP2010 gas chromatography-mass spectrometer (GC-2010 coupled with a G.C.–MS QP-2010 Ultra) equipped with an auto-sampler (AOC-20i+s) from Shimadzu (Tokyo, Japan), using Rtx-5ms column (30 m length × 0.25 mm inner diameter × 0.25 μm film thickness; Restek, Bellefonte, PA, USA). Helium (99.9% purity) was used as the carrier gas with a 1 mL/min column flow rate. The column temperature regime was initially adjusted at 35°C for 2 min, followed by an increase in a rate of 10°C/min to reach 250°C. The temperature was then increased by 20°C/min until reaching 320°C and kept for 23 min. The injection volume and injection temperatures were 1 μL and 250°C using splitless injection mode, respectively. The mass spectrometer operated in electron impact mode with electron energy of 70 eV. The ion source temperature and the interface temperature were set at 240°C and 250°C, respectively. The MS mode was set on scan mode starting from 50–650 m/z with a scan speed of 1428. Data collection and analysis were performed using MSD Enhanced Chemstation software (Shimadzu). G.C. total ion chromatograms (TIC) and fragmentation patterns of the compound were identified using the NIST/EPA/NIH Mass Spectral Library (NIST 14) (S1 File). The run time for each sample was 43.67 min [31].

Preprocessing gas chromatography-mass spectrometry (GC-MS) data

Preprocessing of metabolomics data was performed using an in-house R script. Probabilistic Quotient Normalization [32] normalizes data due to dilution effects in the extraction procedure using the function normalization in the R package KODAMA [33]. The number of missing metabolites in the three replicates of each condition (i.e., drug and cell line) was counted. When the number was equal to 3, missing values were imputed with zero; otherwise, missing values were imputed using the k-nearest neighbor (kNN) algorithm [34], with k = 2. By limiting the kNN imputation to the metabolites with at least two values out of 3/condition, imputation using the information from different conditions (e.g., treated and non-treated) was avoided.

Data analysis and statistical approach

DEGseq [35] and PossionDis [36] algorithms were used to detect the differentially expressed genes (DEG) between samples and groups. Hierarchical clustering for DEGs was performed using the R function heatmap. Mfuzz [37] v2.34.0 was used to cluster gene expression data for time series. Overrepresentation analysis (ORA) was used to determine the gene ontology (G.O.) functional enrichment of DEGs using the hypergeometric test using the R function phyper. Then we calculate the false discovery rate (FDR) for each p-value. In general, it was defined as significantly enriched in terms where FDR was not larger than 0.01. Principal component analysis (PCA) was used to visualize the metabolomic data. Data were mean-centered and scaled to unit variance before PCA. Metabolite set enrichment analysis (MSEA) was carried out using the Gene Set Enrichment Analysis (GSEA) algorithm [38]. The metabolite sets were built using the substituents characterization provided by the Human Metabolome Database [39]. The ranking in the MSEA was performed by using the coefficient of the first principal component of PCA. Two-way ANOVA was used to compare the strains, treatment and to investigate their interaction. The threshold for significance was p < 0.05 to account for multiple testing, a false discovery rate (FDR) was calculated using the q conversion algorithm in multiple comparisons. For metabolomics analysis, an FDR of <5% was chosen to reduce the identification of false positives. All data, including the raw QGD files, has been deposited to Metabolomics Workbench (https://www.metabolomicsworkbench.org). The data track ID is 2026.

Results

Transcriptomic changes in the C. albicans ppg1Δ/Δ strain growing under strong filament-inducing conditions

The pattern of gene expression in wild-type (DK318) and ppg1Δ/Δ (MAY34) strains of C. albicans growing under filament-inducing (10% fetal bovine serum at 37°C) at different time points post culture (3 and 5 hours) was investigated as means of tracking any transcriptional changes that occur during the morphological transition from yeast to hyphae. The filamentation phenotype of both wild-type (W.T.) and ppg1Δ/Δ C. albicans strains under filamentation induction conditions was confirmed microscopically and was consistent with previously published observations [28]. As expected, all cells grew as yeast at the 37°C non-inducing control condition, just before induction and their gene expression profile shows comparable results between ppg1Δ/Δ and W.T. strains. A Scatter plot of DEGs analyses of the RNA sequencing data was used to visualize the transcriptomic changes. The ppg1Δ/Δ strain relative to W.T. (W.S. vs. P.S.) at 5 hours post-induction with serum at 37°C and showed a significant effect for ppg1Δ/Δ mutation on C. albicans gene expression at the 5-hour time point, genes that showed >2-fold change in expression levels were considered as differentially expressed. A total of 2,158 genes showed a significant difference in expression between WS5 and PS5 (Fig 1A), more detalied information can be found at the S1 File. Based on these parameters, 1448 DEGs were upregulated, and 710 were downregulated in the ppg1Δ/Δ samples relative to W.T. control at the 5 hr. time point. We further classified the identified DEGs according to the proposed functional pathway, 1061 DEGs identified under metabolism KEGG pathways (Fig 1B).

[Figure omitted. See PDF.]

Fig 1. Transcriptomic changes of C. albicans ppg1Δ/Δ strain growing under strong filament-inducing condition, 10% serum at 37°C.

(A) Scatter plots of DEGs, X and Y axes represent log10 transformed gene expression level, red color represents significantly upregulated genes, and blue color represents significantly down-regulated genes in W.S. and P.S. at the 5 hr. time point. (B) Pathway classification of DEGs. The X-axis represents the number of DEG. Y-axis represents the functional classification of KEGG. There are seven KEGG pathways: Cellular Processes, Environmental Information Processing, Genetic Information Processing, Metabolism, Organismal Systems.

https://doi.org/10.1371/journal.pone.0259588.g001

PPG1 modulates the expression of multiple filament-specific and central carbon metabolisms genes in response to serum at 37°C: To identify essential gene targets affected by PPG1, we examined genes with a four-fold or more change expression between W.S. and P.S. (Table 2).

[Figure omitted. See PDF.]

Table 2. List of selected gene targets with a four-fold or more change in expression in ppg1 Δ/Δ strain.

https://doi.org/10.1371/journal.pone.0259588.t002

This list of genes included filament-specific genes such as ALS3, HWP1, ECE1, RBT1, and genes involved in C. albicans central carbon metabolisms such as GDH3, GPD1, GPD2, RHR2, INO1, AAH1, and MET14.

We further explored top enriched G.O. terms from DEGs based on various metabolic processes and signaling pathways. To further explore the effect of PPG1 on various biological functions, we determined the functional pathway enrichment of DEGs (Fig 2A). In order to detect which metabolic pathways were affected by PPG1, we conducted a Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. This analysis detected a total of 20 enriched KEGG pathways that were significantly downregulated in C. albicans ppg1Δ/Δ strain growing under filament-inducing conditions (Fig 2B). The data showed significant enrichment of multiple metabolic pathways, such as those involved in sugar (galactose) (P-value 0.0003, FDR 3.965E-03), amino acid (P-value 0.0009, FDR 8.7187E-03), nucleotide (purines) (P-value 0.0023, FDR 1.987E-02), and fatty acid (alpha-linolenic acid) metabolism (P-value 9.357E-05, FDR 2.795E-03) among others (Fig 2B).

[Figure omitted. See PDF.]

Fig 2. Global transcriptomic analysis and pathway functional enrichment of DEGs in C. albicans ppg1 Δ/Δ strain growing under strong filament-inducing condition.

(A) Bar graph representation of significantly up/down-regulated genes, X-axis, represents G.O. term. Y-axis represents the amount of retained G.O. terms for biological processes in RNA sequencing analysis in response to filament-induction. (B) The X-axis represents the enrichment factor. Y-axis represents the pathway name. Rich Factor refers to the value of enrichment factor, the quotient of foreground value (the number of DEGs), and background value (total gene amount). The larger the value, the more significant enrichment.

https://doi.org/10.1371/journal.pone.0259588.g002

Metabolomics analysis of C. albicans ppg1Δ/Δ strain growing under strong filament-inducing.

Using GC-MS, we profiled 35 metabolites (15 sugars and 20 amino acids). First, we examined the variance in the metabolic profiles among different samples using PCA (Fig 3A).

[Figure omitted. See PDF.]

Fig 3.

(A) PCA of the metabolomics profile in C. albicans under filament-inducing (10% fetal bovine serum at 37°C) and control for wild-type C. albicans (DK318) and ppg1Δ/Δ (MAY 34) strains. Cells were harvested at three and five hours. (B) The relative concentration of lactic acid. PC1 = first principal component; PC2 = second principal component.

https://doi.org/10.1371/journal.pone.0259588.g003

The first component (PC1) accounted for 34.5% of the total variance in the data set, with a further 22.2% explained by the second component (PC2). In the first component, we noted that the serum treatment had a considerable effect on the metabolic profile of the W.T. strains. However, this effect was not observable in the treatment of the ppg1Δ/Δ strain. Then, we performed enrichment analysis on the metabolites that contributed to determining the first principal component scores using their substituent characterization. According to the pre-ranked MSEA based on the first principal component’s coefficient, we observed a negative enrichment of metabolites with carboxylic acid substituents (p-value = 5.75x10-3; FDR = 1.32x10-2) and a positive enrichment of metabolites with pyranose substituents (p-value = 5.60x10-2; FDR = 4.47x10-1). More detailed results of the 26 substituents metabolite sets are provided in Table 3. Further, we did not detect a significant difference between both strains at non-inducing control conditions.

[Figure omitted. See PDF.]

Table 3. List of metabolites associated with Ppg1.

https://doi.org/10.1371/journal.pone.0259588.t003

Among the metabolites included in the carboxylic acid substituent group, we observed a statistically significant interaction between genotype (ppg1Δ/Δ, W.T. strains) and treatment with (serum at 37°C) on the lactic acid levels (p-value = 1.77x10-2; FDR = 1.27x10-1) using ANOVA. Indeed, we observed a complete depletion of lactic acid only in the W.T. strain when treated with serum (Fig 3B). ANOVA analysis on all metabolites is reported in S3 Table in S1 File.

Discussion

Phosphorylation is an essential post-translational modification step that is highly conserved across all eukaryotes’ signaling events. In Candida, kinases drive most cellular biologic functions, including metabolism, filamentation, and virulence [40]. Phosphatase (dephosphorylation) counters incessant kinase activity and maintains cellular homeostasis in response to different environmental stimuli [41]. This study provides new insight into the contribution of phosphatases in C. albicans morphogenesis utilizing transcriptomic and metabolomics approaches. Ppg1, a serine/threonine protein phosphatase, plays a vital role in controlling C. albicans morphology and virulence [28]. To further explore the poorly understood role of phosphateses in C albicans morphology and virulence, we carried out detailed transcriptomic and metabolomics profiling of wild-type and ppg1 mutants strains of the pathogen. The data showed that C. albicans ppg1 Δ/Δ strain growing under strong filament-inducing conditions undergo significant transcriptomic changes. The hierarchical clustering and scatter plot of DEGs showed changes in >35% of the entire Candida genome, 1448 upregulated genes, and 710 downregulated genes compared to the W.T. control. Consistent with previous findings, this significant transcriptional change suggests an important, and possibly a master regulator, the role of PPG1 in filament extension, among other potential roles [28,41,42]. PPG1 role is not surprising given that ser/thr phosphatases consist of multiprotein complexes with significant structural diversity that provides for an expansive array of regulatory roles in multiple signaling events.

Among the critical targets for the Ppp1 regulatory effect were filament-specific and central carbon metabolism pathways. Consistent with the previous analysis, the most downregulated genes in ppg1 Δ/Δ C. albicans grown at 37°C were genes involved in filamentation and virulence, such as ALS3, HWP1, ECE1, and RBT1 [43]. It is well accepted that C. albicans cells utilize Als3 (a member of the agglutinin-like sequence adhesins) during filamentation for epithelial adhesion [44]. Moreover, Als3 plays a significant role in iron acquisition, which is critical for fungal pathogenesis [45]. Interestingly, we noticed an upregulation in the expression of the iron transport gene FET31 in C. albicans ppg1Δ/Δ strain. FET31 upregulation could reflect increased metabolism [46] in the ppg1Δ/Δ strain as suggested by the transcriptomic profile shown in Figs 1 and 2. Furthermore, our data suggest that PPG1 could function as a significant regulator given that it increased the expression of multiple carbon metabolism genes, including GDH3, GPD1, GPD2, RHR2, INO1, AAH1, and MET14. GDH3 (NADPH-dependent glutamate dehydrogenase). This group of genes is collectively essential for nitrogen metabolism, the maintenance of the redox balance, and C. albicans filament formation [47,48]. For example, GPD1 and GPD2 (two isoforms of glycerol 3-phosphate dehydrogenase) are rate-controlling enzymes in essential glycerol formation reactions in Saccharomyces cerevisiae [49]. They also play a crucial role in osmoregulation, carbohydrate metabolism, and redox balancing [47,50]. It is worth noting that both Gpd1 and Gpd2 are negatively regulated by the phosphorylation activity of the AMP-activated protein kinase Snf1, the TORC2-dependent kinases Ypk1 and Ypk2 possibly in a Ppg1-dependant manner [51]. Additionally, Candida glycerol 3-phosphate dehydrogenases help the pathogen evade the immune response through their ability to interact with the vital complement regulators, including H and H–like factors [52]. RHR2 (glycerol 3-phosphatase) is also essential for osmotic stress, glycerol accumulation, biofilm formation, and yeast-hyphal switch [53,54]. INO1 (Inositol-1-phosphate synthase), which is vital for inositol synthesis, is considered as a growth factor that supports the formation of glycophosphatidylinositol (GPI)-anchored glycolipids on Candida cell surface and hence the promotion of pathogenesis [55,56]. AAH1 (an Adenine deaminase) is similar to serine/threonine dehydratases essential for purine salvage and nitrogen catabolism [47]. MET14 (an adenylylsulfate kinase) is essential for assimilating sulfate to sulfide, which strongly depends on yeast growth conditions such as glucose [57].

Our data showed that a total of 20 enriched KEGG pathways were significantly downregulated in C. albicans ppg1Δ/Δ strain growing under filament-inducing conditions (Fig 2B). These mainly included pathways involved in sugars (galactose) and amino acid biosynthesis and purine metabolism, all of which are essential for filament extension and virulence [58,59]. Interestingly, the highest pathway enrichment in C. albicans ppg1Δ/Δ strain grown under filament growth conditions concerns the metabolism of alpha-Linolenic acid (ALA), a known inhibitor of hyphal growth C. albicans. Previous transcriptional profiling revealed that ALA downregulates hypha-specific genes in a UME6 and RFG1 (hyphal transcriptional regulators) independent manner. Perhaps ALA functions through a Ppg1- dependent mechanism [60].

Based on the above-noted discussion, we sought to explore further the effect of PPG1 on C. albicans metabolic functions using GC-MS metabolomics analysis. We profiled 35 metabolites, including sugars and amino acids. Enrichment analysis showed an adverse enrichment profile of metabolites with carboxylic acid substituents in C. albicans ppg1Δ/Δ strain growing under strong filament-inducing conditions and consistent with the downregulation of Jen2 (a dicarboxylic acid transporter protein), which was previously shown to be regulated by glucose repression in C. albicans as shown in Table 1 [61]. C. albicans uses carboxylic acids substituents including acetate and lactate for survival and evasion of phagocytosis [62,63]. For example, macrophages-mediated phagocytosis during systemic candidiasis in mice was reported to induce lactate and acetate transporters [63]. Positive enrichment of metabolites with pyranose substituents in C. albicans ppg1Δ/Δ strain growing under strong filament-inducing conditions consistent with the upregulation of genes involved in central carbon metabolism and hence fungal pathogenicity [47]. Glycans are critical for fungal pathogenesis owing to their well-established roles in cell adhesion, immune cell evasion, and inhibition of lymphoproliferation [64–66]. Mannans as a vital component of the fungal cell wall, are also of significance in fungal virulence; inactivation of genes involved in mannan biosynthesis was previously linked to decreased virulence of C. albicans [67]. Altogether, Ppg1 seems to exhibit a master regulator role that influences lactic acid and carboxylic acid utilization and the conversion of pyranose to sugars that could be utilized to synthesize filamentation-related cell wall polysaccharides.

In conclusion, the data presented here elaborated on the role of phosphatases such as PPG1 in regulating the morphological transition of C. albicans at the transcriptional level. PPG1 affected the expression of >35% of the Candida genus, especially those involved in or associated with C. albicans pathogenesis, filamentation, and metabolic activities. Shedding more light on the regulatory events that ensue during C. albicans filamentous growth and virulence may lead to novel antifungal therapeutic strategies. Further work is still needed to validate the findings presented by this study using PPG1 mutant-induced animal models. Further analysis of the role of Ppg1 in protein glycosylation and other virulence-related events such as biofilm formation and immune evasion is also warranted.

Supporting information

S1 File. Supporting file contains all the supporting tables and figures, supplementary file.

S1 Fig. GC-MS total ion chromatograms (TIC) of metabolites extract from Candida albicans. S1 Table. Clean reads quality metrics. S2 Table. Summary of Genome Mapping Ratio. S3 Table. Metabolites interplay between ppg1Δ/Δ and W.T. strains.

https://doi.org/10.1371/journal.pone.0259588.s001

(XLSX)

Acknowledgments

We are grateful to David Kadosh (U.T. Health, San Antonio), USA, to provide C. albicans strains.

Citation: A. L. Bataineh MT, Soares NC, Semreen MH, Cacciatore S, Dash NR, Hamad M, et al. (2021) Candida albicans PPG1, a serine/threonine phosphatase, plays a vital role in central carbon metabolisms under filament-inducing conditions: A multi-omics approach. PLoS ONE 16(12): e0259588. https://doi.org/10.1371/journal.pone.0259588

References

1. Edmond MB, Wallace SE, McClish DK, Pfaller MA, Jones RN, Wenzel RP. Nosocomial bloodstream infections in United States hospitals: a three-year analysis. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America. 1999;29(2):239–44. Epub 1999/09/07. pmid:10476719.

2. Wisplinghoff H, Bischoff T, Tallent SM, Seifert H, Wenzel RP, Edmond MB. Nosocomial bloodstream infections in U.S. hospitals: analysis of 24,179 cases from a prospective nationwide surveillance study. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America. 2004;39(3):309–17. Epub 2004/08/13. pmid:15306996.

3. Odds F.C. Pathogenesis of Candida infections. Journal of the American Academy of Dermatology. 1994;31(3 Pt 2):S2–5. Epub 1994/09/01. pmid:8077502.

4. Mayer FL, Wilson D, Hube B. Candida albicans pathogenicity mechanisms. Virulence. 2013;4(2):119–28. Epub 2013/01/11. pmid:23302789; PubMed Central PMCID: PMC3654610.

5. Hebecker B, Naglik JR, Hube B, Jacobsen ID. Pathogenicity mechanisms and host response during oral Candida albicans infections. Expert review of anti-infective therapy. 2014;12(7):867–79. Epub 2014/05/08. pmid:24803204.

6. Hartland CL, Youngsaye W, Morgan B, Ting A, Nag P, Buhrlage S, et al. Identification of small molecules that selectively inhibit fluconazole-resistant Candida albicans in the presence of fluconazole but not in its absence—Probe 2. Probe Reports from the NIH Molecular Libraries Program. Bethesda (MD)2010.

7. Scorzoni L, de Paula ESAC, Marcos CM, Assato PA, de Melo WC, de Oliveira HC, et al. Antifungal Therapy: New Advances in the Understanding and Treatment of Mycosis. Frontiers in microbiology. 2017;8:36. Epub 2017/02/09. pmid:28167935; PubMed Central PMCID: PMC5253656.

8. Biswas S, Van Dijck P, Datta A. Environmental sensing and signal transduction pathways regulating morphopathogenic determinants of Candida albicans. Microbiology and molecular biology reviews: MMBR. 2007;71(2):348–76. Epub 2007/06/08. pmid:17554048; PubMed Central PMCID: PMC1899878.

9. Mitchell AP. Dimorphism and virulence in Candida albicans. Current opinion in microbiology. 1998;1(6):687–92. Epub 1999/03/06. pmid:10066539.

10. Grainha TRR, Jorge P, Perez-Perez M, Perez Rodriguez G, Pereira M, Lourenco AMG. Exploring anti-quorum sensing and anti-virulence based strategies to fight Candida albicans infections: an in silico approach. FEMS yeast research. 2018;18(3). Epub 2018/03/09. pmid:29518242.

11. Sherwood J, Gow NA, Gooday GW, Gregory DW, Marshall D. Contact sensing in Candida albicans: a possible aid to epithelial penetration. Journal of medical and veterinary mycology: bi-monthly publication of the International Society for Human and Animal Mycology. 1992;30(6):461–9. Epub 1992/01/01. pmid:1287165.

12. Mu C, Pan C, Han Q, Liu Q, Wang Y, Sang J. Phosphatidate phosphatase Pah1 has a role in the hyphal growth and virulence of Candida albicans. Fungal genetics and biology: FG & B. 2019;124:47–58. Epub 2019/01/08. pmid:30615943.

13. Kumamoto CA, Vinces MD. Contributions of hyphae and hypha-co-regulated genes to Candida albicans virulence. Cellular microbiology. 2005;7(11):1546–54. Epub 2005/10/07. pmid:16207242.

14. Aoki W, Kitahara N, Miura N, Morisaka H, Yamamoto Y, Kuroda K, et al. Comprehensive characterization of secreted aspartic proteases encoded by a virulence gene family in Candida albicans. Journal of biochemistry. 2011;150(4):431–8. Epub 2011/06/08. pmid:21646240.

15. Sturtevant J, Calderone R. Candida albicans adhesins: Biochemical aspects and virulence. Revista iberoamericana de micologia. 1997;14(3):90–7. Epub 2007/07/28. pmid:17655381.

16. Duhring S, Germerodt S, Skerka C, Zipfel PF, Dandekar T, Schuster S. Host-pathogen interactions between the human innate immune system and Candida albicans-understanding and modeling defense and evasion strategies. Frontiers in microbiology. 2015;6:625. Epub 2015/07/16. pmid:26175718; PubMed Central PMCID: PMC4485224.

17. Sudbery PE. Growth of Candida albicans hyphae. Nature reviews Microbiology. 2011;9(10):737–48. Epub 2011/08/17. pmid:21844880.

18. Albataineh MT, Kadosh D. Regulatory roles of phosphorylation in model and pathogenic fungi. Medical mycology. 2016;54(4):333–52. Epub 2015/12/27. pmid:26705834; PubMed Central PMCID: PMC4818690.

19. Day AM, Quinn J. Stress-Activated Protein Kinases in Human Fungal Pathogens. Frontiers in cellular and infection microbiology. 2019;9:261. Epub 2019/08/06. pmid:31380304; PubMed Central PMCID: PMC6652806.

20. Han T-l, Cannon RD, Villas-Bôas SG. Metabolome analysis during the morphological transition of Candida albicans. Metabolomics: Official journal of the Metabolomic Society. 2012;8(6):1204–17.

21. Li L, Liao Z, Yang Y, Lv L, Cao Y, Zhu Z. Metabolomic profiling for the identification of potential biomarkers involved in a laboratory azole resistance in Candida albicans. PloS one. 2018;13(2):e0192328. pmid:29394282

22. Noble SM, French S, Kohn LA, Chen V, Johnson AD. Systematic screens of a Candida albicans homozygous deletion library decouple morphogenetic switching and pathogenicity. Nature genetics. 2010;42(7):590–8. Epub 2010/06/15. pmid:20543849; PubMed Central PMCID: PMC2893244.

23. Evans DR, Stark MJ. Mutations in the Saccharomyces cerevisiae type 2A protein phosphatase catalytic subunit reveal roles in cell wall integrity, actin cytoskeleton organization and mitosis. Genetics. 1997;145(2):227–41. Epub 1997/02/01. PubMed Central PMCID: PMC1207790. pmid:9071579

24. Garbers C, DeLong A, Deruere J, Bernasconi P, Soll D. A mutation in protein phosphatase 2A regulatory subunit A affects auxin transport in Arabidopsis. The EMBO journal. 1996;15(9):2115–24. Epub 1996/05/01. PubMed Central PMCID: PMC450134. pmid:8641277

25. Lee CM, Nantel A, Jiang L, Whiteway M, Shen SH. The serine/threonine protein phosphatase SIT4 modulates yeast-to-hypha morphogenesis and virulence in Candida albicans. Molecular microbiology. 2004;51(3):691–709. Epub 2004/01/21. pmid:14731272.

26. Furukawa K, Fukuda T, Yamashita SI, Saigusa T, Kurihara Y, Yoshida Y, et al. The PP2A-like Protein Phosphatase Ppg1 and the Far Complex Cooperatively Counteract CK2-Mediated Phosphorylation of Atg32 to Inhibit Mitophagy. Cell reports. 2018;23(12):3579–90. Epub 2018/06/21. pmid:29925000.

27. Banerjee M, Thompson DS, Lazzell A, Carlisle PL, Pierce C, Monteagudo C, et al. UME6, a novel filament-specific regulator of Candida albicans hyphal extension and virulence. Molecular biology of the cell. 2008;19(4):1354–65. Epub 2008/01/25. pmid:18216277; PubMed Central PMCID: PMC2291399.

28. Albataineh MT, Lazzell A, Lopez-Ribot JL, Kadosh D. Ppg1, a PP2A-Type protein phosphatase, controls filament extension and virulence in candida albicans. Eukaryotic Cell. 2014;13(12):1538–47. pmid:25326520

29. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nature methods. 2012;9(4):357–9. Epub 2012/03/06. pmid:22388286; PubMed Central PMCID: PMC3322381.

30. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC bioinformatics. 2011;12:323. Epub 2011/08/06. pmid:21816040; PubMed Central PMCID: PMC3163565.

31. Semreen MH, Soliman SSM, Saeed BQ, Alqarihi A, Uppuluri P, Ibrahim AS. Metabolic Profiling of Candida auris, a Newly-Emerging Multi-Drug Resistant Candida Species, by GC-MS. Molecules. 2019;24(3). Epub 2019/01/27. pmid:30678308; PubMed Central PMCID: PMC6384714.

32. Dieterle F, Ross A, Schlotterbeck G, Senn H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Analytical chemistry. 2006;78(13):4281–90. pmid:16808434

33. Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. KODAMA: an R package for knowledge discovery and data mining. Bioinformatics. 2017;33(4):621–3. pmid:27993774

34. Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, et al. Missing value estimation methods for DNA microarrays. Bioinformatics. 2001;17(6):520–5. pmid:11395428

35. Wang L, Feng Z, Wang X, Zhang X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics. 2010;26(1):136–8. Epub 2009/10/27. pmid:19855105.

36. Audic S, Claverie JM. The significance of digital gene expression profiles. Genome research. 1997;7(10):986–95. Epub 1997/10/23. pmid:9331369

37. Kumar L M E.F. Mfuzz: a software package for soft clustering of microarray data. Bioinformation. 2007;2(1):5–7. Epub 2007/12/18. pmid:18084642; PubMed Central PMCID: PMC2139991.

38. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences. 2005;102(43):15545–50. pmid:16199517

39. Wishart D, Feunang Y, Marcu A, Guo A, Liang K. vázquez-Fresno R. Sajed T. 2017.

40. Albataineh MT, Kadosh D. Regulatory roles of phosphorylation in model and pathogenic fungi. Sabouraudia. 2015;54(4):333–52. pmid:26705834

41. Albataineh MT, Kadosh D. Regulatory roles of phosphorylation in model and pathogenic fungi. Med Mycol. 2016;54(4):333–52. pmid:26705834

42. Offley SR, Schmidt MC. Protein phosphatases of Saccharomyces cerevisiae. Current genetics. 2019;65(1):41–55. Epub 2018/09/19. pmid:30225534; PubMed Central PMCID: PMC6344269.

43. Martin R, Albrecht-Eckardt D, Brunke S, Hube B, Hunniger K, Kurzai O. A core filamentation response network in Candida albicans is restricted to eight genes. PloS one. 2013;8(3):e58613. Epub 2013/03/22. pmid:23516516; PubMed Central PMCID: PMC3597736.

44. Hoyer LL, Payne TL, Hecht JE. Identification of Candida albicans ALS2 and ALS4 and localization of als proteins to the fungal cell surface. Journal of bacteriology. 1998;180(20):5334–43. Epub 1998/10/10. pmid:9765564; PubMed Central PMCID: PMC107581.

45. Almeida RS, Brunke S, Albrecht A, Thewes S, Laue M, Edwards JE, et al. the hyphal-associated adhesin and invasin Als3 of Candida albicans mediates iron acquisition from host ferritin. PLoS pathogens. 2008;4(11):e1000217. Epub 2008/11/22. pmid:19023418; PubMed Central PMCID: PMC2581891.

46. Eck R, Hundt S, Hartl A, Roemer E, Kunkel W. A multicopper oxidase gene from Candida albicans: cloning, characterization and disruption. Microbiology (Reading). 1999;145 (Pt 9):2415–22. Epub 1999/10/12. pmid:10517594.

47. Han TL, Cannon RD, Villas-Boas SG. The metabolic basis of Candida albicans morphogenesis and quorum sensing. Fungal genetics and biology: F.G. & B. 2011;48(8):747–63. Epub 2011/04/26. pmid:21513811.

48. Han TL, Cannon RD, Gallo SM, Villas-Boas SG. A metabolomic study of the effect of Candida albicans glutamate dehydrogenase deletion on growth and morphogenesis. NPJ biofilms and microbiomes. 2019;5(1):13. Epub 2019/04/18. pmid:30992998; PubMed Central PMCID: PMC6453907 non-financial relationships that could be construed as a potential conflict of interest.

49. Fitzmaurice C, Abate D, Abbasi N, Abbastabar H, Abd-Allah F, Abdel-Rahman O, et al. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2017: A Systematic Analysis for the Global Burden of Disease Study. JAMA oncology. 2019. Epub 2019/09/29. PubMed Central PMCID: PMC6777271. pmid:31560378

50. Hubmann G, Guillouet S, Nevoigt E. Gpd1 and Gpd2 fine-tuning for sustainable reduction of glycerol formation in Saccharomyces cerevisiae. Applied and environmental microbiology. 2011;77(17):5857–67. Epub 2011/07/05. pmid:21724879; PubMed Central PMCID: PMC3165387.

51. Lee YJ, Jeschke GR, Roelants FM, Thorner J, Turk BE. Reciprocal phosphorylation of yeast glycerol-3-phosphate dehydrogenases in adaptation to distinct types of stress. Molecular and cellular biology. 2012;32(22):4705–17. Epub 2012/09/19. pmid:22988299; PubMed Central PMCID: PMC3486180.

52. Luo S, Hoffmann R, Skerka C, Zipfel PF. Glycerol-3-phosphate dehydrogenase 2 is a novel factor H-, factor H-like protein 1-, and plasminogen-binding surface protein of Candida albicans. The Journal of infectious diseases. 2013;207(4):594–603. Epub 2012/12/04. pmid:23204165.

53. Desai JV, Bruno VM, Ganguly S, Stamper RJ, Mitchell KF, Solis N, et al. Regulatory role of glycerol in Candida albicans biofilm formation. mBio. 2013;4(2):e00637–12. Epub 2013/04/11. pmid:23572557; PubMed Central PMCID: PMC3622937.

54. Desai JV, Cheng S, Ying T, Nguyen MH, Clancy CJ, Lanni F, et al. Coordination of Candida albicans Invasion and Infection Functions by Phosphoglycerol Phosphatase Rhr2. Pathogens. 2015;4(3):573–89. Epub 2015/07/28. pmid:26213976; PubMed Central PMCID: PMC4584273.

55. Chen YL, Kauffman S, Reynolds TB. Candida albicans uses multiple mechanisms to acquire the essential metabolite inositol during infection. Infection and immunity. 2008;76(6):2793–801. Epub 2008/02/13. pmid:18268031; PubMed Central PMCID: PMC2423082.

56. Jin JH, Seyfang A. High-affinity myo-inositol transport in Candida albicans: substrate specificity and pharmacology. Microbiology (Reading). 2003;149(Pt 12):3371–81. Epub 2003/12/10. pmid:14663071.

57. Donalies UE, Stahl U. Increasing sulphite formation in Saccharomyces cerevisiae by overexpression of MET14 and SSU1. Yeast. 2002;19(6):475–84. Epub 2002/03/29. pmid:11921096.

58. Martin R, Albrecht-Eckardt D, Brunke S, Hube B, Hünniger K, Kurzai O. A core filamentation response network in Candida albicans is restricted to eight genes. PloS one. 2013;8(3):e58613. pmid:23516516

59. Han T-L, Cannon RD, Villas-Bôas SG. The metabolic response of Candida albicans to farnesol under hyphae-inducing conditions. FEMS yeast research. 2012;12(8):879–89. pmid:22846172

60. Shareck J, Nantel A, Belhumeur P. Conjugated linoleic acid inhibits hyphal growth in Candida albicans by modulating Ras1p cellular levels and downregulating TEC1 expression. Eukaryotic Cell. 2011;10(4):565–77. pmid:21357478

61. Sexton JA, Brown V, Johnston M. Regulation of sugar transport and metabolism by the Candida albicans Rgt1 transcriptional repressor. Yeast. 2007;24(10):847–60. Epub 2007/07/03. pmid:17605131.

62. Vieira N, Casal M, Johansson B, MacCallum DM, Brown AJ, Paiva S. Functional specialization and differential regulation of short‐chain carboxylic acid transporters in the pathogen Candida albicans. Molecular microbiology. 2010;75(6):1337–54. pmid:19968788

63. Lorenz MC, Bender JA, Fink GR. Transcriptional response of Candida albicans upon internalization by macrophages. Eukaryotic Cell. 2004;3(5):1076–87. pmid:15470236

64. Buurman ET, Westwater C, Hube B, Brown AJ, Odds FC, Gow NA. Molecular analysis of CaMnt1p, a mannosyl transferase important for adhesion and virulence of Candida albicans. Proceedings of the National Academy of Sciences. 1998;95(13):7670–5. pmid:9636208

65. García-Carnero LC, Martínez-Álvarez JA, Salazar-García LM, Lozoya-Pérez NE, González-Hernández SE, Tamez-Castrellón AK. Recognition of Fungal Components by the Host Immune System. Current Protein and Peptide Science. 2020;21(3):245–64. pmid:31889486

66. Nelson R, Shibata N, Podzorski R, Herron M. Candida mannan: chemistry, suppression of cell-mediated immunity, and possible mechanisms of action. Clinical microbiology reviews. 1991;4(1):1–19. pmid:2004345

67. West L, Lowman DW, Mora-Montes HM, Grubb S, Murdoch C, Thornhill MH, et al. Differential virulence of Candida glabrata glycosylation mutants. The Journal of biological chemistry. 2013;288(30):22006–18. Epub 2013/05/31. pmid:23720756; PubMed Central PMCID: PMC3724654.