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

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.

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.

Table 1

Data description.

Sample ID	Description	Strain
PC3	ppg1Δ/Δ strain at 3 hrs. under 30°C control	MAY34
PC5	ppg1Δ/Δ strain at 5 hrs. under 30°C control	MAY34
PS3	ppg1Δ/Δ strain at 3 hrs. under 37°C + serum	MAY34
PS5	ppg1Δ/Δ strain at 5 hrs. under 37°C + serum	MAY34
WC3	Wild-type strain at 3 hrs. under 30°C control	DK318
WC5	Wild-type strain at 5 hrs. under 30°C control	DK318
WS3	Wild-type strain at 3 hrs. under 37°C +serum	DK318
WS5	Wild-type strain at 5 hrs. under 37°C +serum	DK318

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).

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.

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).

Table 2

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

Gene Symbol	log2 Fold Change	P-Value	FDR	Gene Function
GDH3	7.77957	0.000	0.000	oxidoreductase activity, acting on the CH-NH2 group of donors, NAD or NADP as acceptor glutamate dehydrogenase (NADP+) activity
INO1	7.202084	0.000	0.000	inositol-3-phosphate synthase activity phospholipid biosynthetic process
AAH1	5.312624	1.14E-65	5.10E-65	metal ion binding adenine deaminase activity
GPD2	4.991099	0.000	0.000	NAD binding protein homodimerization activity
MET14	4.75566	0.000	0.000	ATP binding adenylylsulfate kinase activity
GPD1	4.688903	0.000	0.000	NAD binding protein homodimerization activity
RHR2	4.653127	0.000	0.000	hydrolase activity glycerol-3-phosphatase activity
JEN2	-13.4639	0.000	0.000	dicarboxylic acid transmembrane transporter activity transmembrane transporter activity
HWP1	-5.2734	1.03E-105	6.22E-105	hyphal cell wall adhesion molecule binding
ECE1	-4.84915	3.54E-69	1.66E-69	hypha-specific protein with toxin activity
ALS3	-4.80271	1.16E-19	2.81E-19	agglutinin-like sequence adhesins
RBT1	-4.452919	1.49E-19	1.97E-06	cell wall protein

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).

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.

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).

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.

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.

Table 3

List of metabolites associated with Ppg1.

pathway	p-value	FDR	ES	NES	size	metabolites
Carboxylic acid	5.75E-03	1.32E-01	-0.75	-1.77	17	L-Valine/Malic acid/Pipecolic acid/Glyceric acid/Malonic acid/Glycolic acid/2-Aminobenzoic acid/N,N-Dimethylglycine/Stearic acid/2-Keto-d-gluconic acid/Palmitic Acid/Lactic Acid/Galacturonic acid/Succinic acid/Gulonic acid/Pyroglutamic acid/Octadecanoic acid
Pyranose	5.60E-02	4.47E-01	0.85	1.74	4	L-Rhamnose/D-Xylose/D-(+)-Galactose/D-(+)-Turanose
Beta-hydroxy acid	6.61E-02	4.47E-01	-0.80	-1.41	4	Malic acid/Glyceric acid/Galacturonic acid/Gulonic acid
Polyol	8.16E-02	4.47E-01	0.45	1.39	12	Pentitol/Glycerol/2-Keto-d-gluconic acid/L-Rhamnose/Galacturonic acid/D-Xylose/D-(+)-Galactose/Gulonic acid/Scyllo-Inositol/D-(+)-Turanose/Xylitol/Myo-Inositol
Oxacycle	1.17E-01	4.47E-01	0.60	1.38	5	Uridine/L-Rhamnose/D-Xylose/D-(+)-Galactose/D-(+)-Turanose
Amino acid	1.18E-01	4.47E-01	-0.74	-1.31	4	L-Valine/Pipecolic acid/2-Aminobenzoic acid/N,N-Dimethylglycine
Dicarboxylic acid or derivatives	1.36E-01	4.47E-01	-0.80	-1.29	3	Malic acid/Malonic acid/Succinic acid
Fatty acid	1.81E-01	5.20E-01	-0.61	-1.25	7	L-Valine/Malic acid/Galacturonic acid/Succinic acid/Gulonic acid/Palmitic Acid/Octadecanoic acid
Carbonyl group	2.34E-01	5.40E-01	-0.51	-1.21	19	L-Valine/Malic acid/Pipecolic acid/Glyceric acid/Malonic acid/Glycolic acid/N,N-Dimethylglycine/Stearic acid/2-Keto-d-gluconic acid/Palmitic Acid/Lactic Acid/Glycerol monostearate/Galacturonic acid/Succinic acid/Gulonic acid/Pyroglutamic acid/Oleic acid amide/D-(+)-Turanose/Octadecanoic acid
Alpha-hydroxy acid	2.35E-01	5.40E-01	-0.61	-1.20	6	Malic acid/Glyceric acid/Glycolic acid/Lactic Acid/Galacturonic acid/Gulonic acid
Carboxylic acid derivative	3.12E-01	6.27E-01	-0.49	-1.13	13	Malic acid/Glyceric acid/Glycolic acid/2-Aminobenzoic acid/Stearic acid/2-Keto-d-gluconic acid/Palmitic Acid/Lactic Acid/Glycerol monostearate/Galacturonic acid/Gulonic acid/Oleic acid amide/Octadecanoic acid
Fatty acyl	3.48E-01	6.27E-01	-0.62	-1.09	4	L-Valine/Glycerol monostearate/Galacturonic acid/Gulonic acid
Sugar alcohol	3.54E-01	6.27E-01	0.45	1.03	5	Pentitol/Glycerol/Scyllo-Inositol/Xylitol/Myo-Inositol
Alcohol	4.41E-01	7.25E-01	-0.43	-1.03	20	Ergosterol/Octadecanol/Pentitol/Malic acid/Glyceric acid/Glycolic acid/Glycerol/2-Keto-d-gluconic acid/Lactic Acid/Glycerol monostearate/Uridine/Acetoin/Tryptophol/L-Rhamnose/Galacturonic acid/D-Xylose/D-(+)-Galactose/Gulonic acid/D-(+)-Turanose/Xylitol
Organonitrogen compound	5.52E-01	8.47E-01	-0.46	-0.96	8	L-Valine/Pipecolic acid/2-Aminobenzoic acid/N,N-Dimethylglycine/Uridine/Tryptophol/Pyroglutamic acid/Oleic acid amide
Primary alcohol	6.69E-01	8.98E-01	-0.37	-0.86	13	Octadecanol/Pentitol/Glyceric acid/Glycolic acid/Glycerol/2-Keto-d-gluconic acid/Glycerol monostearate/Uridine/Tryptophol/D-(+)-Galactose/Gulonic acid/D-(+)-Turanose/Xylitol
Monosaccharide	6.87E-01	8.98E-01	-0.44	-0.86	6	Pentitol/Glyceric acid/Uridine/Galacturonic acid/Gulonic acid/Xylitol
Cyclic alcohol	7.14E-01	8.98E-01	-0.53	-0.85	3	Ergosterol/Scyllo-Inositol/Myo-Inositol
Azacycle	7.55E-01	8.98E-01	-0.47	-0.83	4	Pipecolic acid/Uridine/Tryptophol/Pyroglutamic acid
Ketone	8.13E-01	8.98E-01	0.41	0.74	3	2-Keto-d-gluconic acid/Acetoin/D-(+)-Turanose
Secondary alcohol	8.20E-01	8.98E-01	-0.30	-0.72	17	Ergosterol/Pentitol/Malic acid/Glyceric acid/Glycerol/2-Keto-d-gluconic acid/Lactic Acid/Uridine/2,3-Butanediol/Acetoin/L-Rhamnose/Galacturonic acid/D-Xylose/D-(+)-Galactose/Gulonic acid/D-(+)-Turanose/Xylitol
Aliphatic heteromonocyclic compound	8.79E-01	9.19E-01	-0.34	-0.67	6	Pipecolic acid/L-Rhamnose/D-Xylose/D-(+)-Galactose/Pyroglutamic acid/D-(+)-Turanose
Organoheterocyclic compound	9.82E-01	9.82E-01	-0.26	-0.53	7	Pipecolic acid/Uridine/L-Rhamnose/D-Xylose/D-(+)-Galactose/Pyroglutamic acid/D-(+)-Turanose

Abbreviations: ES = enrichment score; NES = normalized enrichment score; FDR = false discovery rate.

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 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.

(XLSX)

Click here for additional data file.

28 Jul 2021

PONE-D-21-21871

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

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Two experts in this field thoroughly review this manuscript. Both of them appreciated the quantity and quality of this work, but raised a series of editorial and experimental concerns that the authors should pay attention to. In particular, the authors may need to further analyze their transcriptome and metabolomics data to obtain a better picture of PPG1-dependent signaling networks.

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Reviewer #1: This study performed combined RNA sequencing and metabolomics assay to check the detailed function of PPG1 in filamentation and virulence. Generally, this study showed plenty of data with appropriate analysis, which gives useful information. The manuscript is well written. And the author discussed the potential mechanism by which PPG1 regulates filamentation and virulence logically. But I have some suggestions and comments which may be useful for improving the manuscript.

1. The manuscript supplied enough data but didn't end with a clear story or conclusion. Actually, I still don't know how PPG1 regulate filamentation and virulence. Just saw some genes upregulated and some others downregulated. Therefore, more extended analysis is necessary to tell a logical story.

2. The author directly compares the gene expression between WT and PPG1 KO strains under the FBS inducing conditions. But to find filamentation related genes that are regulated by PPG1, it is better to know the FBS induced transcription in WT group first, and see what kind of genes are not induced or suppressed in KO group. So, I suggest the author should set the groups as WT vs WT+FBS and PPG1KO vs PPG1KO+FBS, if they have the original data.

3. The whole paper supplied enough omics information but with no confirmation. qRT PCR is necessary to confirm the transcription even they used 3 biological replicates, in view of that they neglect the P value or FDR when chosing the changed genes. In particular, the potential targets for PPG1 mentioned in discussion part need to be confirmed.

4. I believe PPG1 regulates the transcription of HWP1, ECE1, ALS3, but the high expression of HSGs, is a common effect by deleting many filamention related genes. And the author try to link the transcription data to metabolomics data, such as the author suggested ALA may function through a Ppg1- dependent mechanism. But I think it is not enough. Some bench work is needed to support the prediction, such as overexpression the targets in PPG1 KO strain and detect the metabolisms.

5. A working model of PPG1 may help to understand its role in filamentation and virulence. PPG1 itself is a serine/threonine phosphatase, a graph contains the substrate and their filamentation targets as well as metabolisms will be helpful for understanding.

Reviewer #2: Summary: The authors have previously characterized Ppg1, a PP2A-type protein phosphatase that controls filament extension and virulence in C. albicans. This study is a follow up analysis of the ppg1Δ/Δ strain in regulating transcriptome relevant to morphogenesis using RNA sequencing analysis. The authors identified that downregulation of well-characterized genes linked to filamentation and virulence as well as the genes involved in the central carbon metabolisms were down regulated in the mutant strain. Their 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. The authors concluded that Ppg1 is a link between metabolites substituents and filament formation controlled by a phosphatase to regulate morphogenesis and virulence. Overall, this manuscript is descriptive, and no functional validation was undertaken to validate the RNA-seq/metabolomics finding. The authors make a case that Ppg1 controls carbon metabolism and filamentation, but the connection to hyphae based on metabolism is not clear.

Comments:

1. The authors conducted large scale transcriptome and metabolome analysis and generated significant amount of data set. They would be able to articulate more defined biological implications of Ppg1 function with a thorough data analysis on the valuable resources they already possess. For example, it would be interesting to see how the transcriptome changes in the wild type or ppg1 mutant strains responding to 30 C control vs. 37 C serum conditions. Just for an overview, they would be able to conduct a PCA analysis (or a hierarchical clustering) of 8 samples presented in Table 1 to compare the overall transcriptome of the wild-type and the ppg1 mutant strain under different conditions.

2. The authors analyzed the transcriptome of the wild type and the ppg1 mutant strains at 3 and 5 hrs post hyphal induction. Based on their previous study published in 2014, the ppg1 mutant demonstrated a similar expression pattern of ALS3, HWP1, and ECE1 at 3 and 5 hrs. However, the expression of these genes (and NRG1) was quite distinctive at 1 or 2 hrs post-induction compared to the wild-type strain. Thus, it is expected that ppg1 mutation would have impacted the transcription of genes at the earlier stage of hyphal induction. I am wondering why the authors did not choose earlier time points for transcriptome analysis. Is there any clear rationale why the authors chose 3 and 5 hrs only? Of those two time points, the authors only used the transcriptome at 5 hrs for their data analysis and the reason was not clearly stated either.

3. Even though the authors demonstrated that Ppg1 is involved in hyphal induction, it is also possible that Ppg1 would play roles in carbon metabolism under yeast condition. Since the authors already have transcriptome data at 30 C, I would recommend comparing the transcriptome of the wild type and the mutant strains under yeast condition as well.

4. What is the control condition for the metabolomic analysis? Is it the same as the control condition for the transcriptome analysis (30C no serum)? The comparison of the control and inducing conditions were not clearly stated in the method or result sections. Please change “PPG1 null” to “ppg1 delta/delta” to be consistent with the transcriptome data.

5. Metabolite normalization: if the ppg1 strain is a slower grower, then the final cell number at the harvest time (after 3 and 5 hrs) would have been different. The presentation of the metabolites/total protein would be more appropriate for normalization.

1. In #360-374, the authors claimed that Ppg1 is involved in central carbon metabolism and cell wall architecture. However, the discussion is descriptive and has no supporting evidence. In S. cerevisiae, Ppg1 homolog is involved in glycogen accumulation. Have the authors tested glycogen accumulation in the mutant strain? If Ppg1 plays a role as a master regulator, as the authors speculated, we would expect to see altered cell wall architecture or cell wall integrity. Have the authors tested for cell wall stressor susceptibility or chitin staining to see any compensatory chitin upregulation in the mutant strain?

6. #214 at the 37C non-inducing condition – 30C?

7. #216 can you further elaborate what “comparable results” means?

8. In consistent wording for time point description: #218, 5 hours; 3219, 5-hour; #223, 5hr.

9. #237 and # 238; PPG1 (Gene), Ppg1 for protein

10. #238, “essential genes” are often used for their functional relevance to cell viability. Did you refer the genes that are directly impacted by Ppg1? Then please use a different term.

11. Table 1, could you include functional categories at the front row of this table? Carbon metabolism, hyphal specific genes, etc.

12. #266 wording in the section title: grown under filament-inducing “condition”

13. #307 “ppg1 mutants” to “ppg1 Δ/Δ”

14. #319 C. albicans to strain, please revise the sentence in #318-320.

15. #325 “increased metabolism” can you specify?

16. #336 Italicize “Candida”

17. #362 add space between “table” and “1”

18. GEO submission numbers are not found.

**********

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Reviewer #2: No

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26 Sep 2021

Academic Editor comments

Two experts in this field thoroughly review this manuscript. Both of them appreciated the quantity and quality of this work, but raised a series of editorial and experimental concerns that the authors should pay attention to. In particular, the authors may need to further analyze their transcriptome and metabolomics data to obtain a better picture of PPG1-dependent signaling networks.

We thank the academic editor and respected reviewers for their time and effort. We have acknowledged and incorporated all reviewers' comments and modified the manuscript when possible. First, we provided a supplementary file detailing differential gene expression comparison between the eight conditions mentioned in table 1. Next, we inserted figure S4 reflecting the transcriptomic changes at 3 hrs. time point. We also modified figure 3 for the metabolomic analysis in response to reviewers' comments. Last, we modified the discussion and conclusion, highlighting the limitations of this study.

Reviewer #1: This study performed combined RNA sequencing and metabolomics assay to check the detailed function of PPG1 in filamentation and virulence. Generally, this study showed plenty of data with appropriate analysis, which gives useful information. The manuscript is well written. And the author discussed the potential mechanism by which PPG1 regulates filamentation and virulence logically. But I have some suggestions and comments which may be useful for improving the manuscript.

1. The manuscript supplied enough data but didn't end with a clear story or conclusion. Actually, I still don't know how PPG1 regulate filamentation and virulence. Just saw some genes upregulated and some others downregulated. Therefore, more extended analysis is necessary to tell a logical story.

We thank reviewer 1 for this suggestion. We have previously characterized PPG1 and demonstrated its importance in filamentation and virulence [1]. In particular, we showed PPG1 importance for filament-specific genes such as ALS3, HWP1, ECE1 by northern analysis. However, it was unclear how PPG1 may regulate the complex regulatory circuits that control morphology and virulence. Therefore, we conducted a global transcriptomic analysis coupled with metabolomics to understand the PPG1 role better. Consistent with previous northern plotting, the transcriptomic analysis identified significant downregulation of well-characterized genes linked to filamentation and virulence, including ALS3, HWP1, ECE1, and RBT1. Further global expression analysis showed important genes involved in C. albicans central carbon metabolisms, including GDH3, GPD1, GPD2, RHR2, INO1, AAH1, and MET14, the top upregulated genes (Table 1). Subsequent metabolomics analysis confirmed the findings from figure 2 (carboxylic acid and pyranose substituents), and then we linked these data with genes from table 1 in the discussion section, such as GDH3 # 326-337 and JEN2 #359-363. Given the eccentric nature of phosphatases and the fact that it most likely affects many other cell biology components, as shown in figure 1A, over 35% of the entire Candida genome was affected. We acknowledge the limitation and complexity of this type of analysis and agree that a more specific analysis is needed to explore different mechanisms in the future, mentioned in # 393-397.

2. The author directly compares the gene expression between WT and PPG1 KO strains under the FBS inducing conditions. But to find filamentation related genes that are regulated by PPG1, it is better to know the FBS induced transcription in WT group first, and see what kind of genes are not induced or suppressed in KO group. So, I suggest the author should set the groups as WT vs WT+FBS and PPG1KO vs PPG1KO+FBS, if they have the original data.

This analysis was done as a baseline control but not shown. Therefore, we added a supplementary file in # 217-220 between the different groups as requested.

3. The whole paper supplied enough omics information but with no confirmation. qRT PCR is necessary to confirm the transcription even they used 3 biological replicates, in view of that they neglect the P value or FDR when chosing the changed genes. In particular, the potential targets for PPG1 mentioned in discussion part need to be confirmed.

In table 1 #251, we did provide the P-value and FDR of the listed genes. We agree with reviewer 1 that a confirmation of the transcriptional changes is needed. However, in this study, we determined metabolic profiles consistent with the well-characterized gene functions from table 1. While this approach may not furnish a direct relationship as acknowledged in limitation # 400-407, it provides insight at the metabolic level for future studies.

4. I believe PPG1 regulates the transcription of HWP1, ECE1, ALS3, but the high expression of HSGs, is a common effect by deleting many filamention related genes. And the author try to link the transcription data to metabolomics data, such as the author suggested ALA may function through a Ppg1- dependent mechanism. But I think it is not enough. Some bench work is needed to support the prediction, such as overexpression the targets in PPG1 KO strain and detect the metabolisms.

We agree with reviewer 1 and removed this prediction from discussion line # 375.

5. A working model of PPG1 may help to understand its role in filamentation and virulence. PPG1 itself is a serine/threonine phosphatase, a graph contains the substrate and their filamentation targets as well as metabolisms will be helpful for understanding.

We agree with reviewer 1, and we can provide a network visualization for predicted protein-protein interactions. However, a more beneficial working model can be generated for a specific mechanism of action as part of the follow-up study.

Reviewer #2: Summary: The authors have previously characterized Ppg1, a PP2A-type protein phosphatase that controls filament extension and virulence in C. albicans. This study is a follow up analysis of the ppg1Δ/Δ strain in regulating transcriptome relevant to morphogenesis using RNA sequencing analysis. The authors identified that downregulation of well-characterized genes linked to filamentation and virulence as well as the genes involved in the central carbon metabolisms were down regulated in the mutant strain. Their 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. The authors concluded that Ppg1 is a link between metabolites substituents and filament formation controlled by a phosphatase to regulate morphogenesis and virulence. Overall, this manuscript is descriptive, and no functional validation was undertaken to validate the RNA-seq/metabolomics finding. The authors make a case that Ppg1 controls carbon metabolism and filamentation, but the connection to hyphae based on metabolism is not clear.

Comments:

1. The authors conducted large scale transcriptome and metabolome analysis and generated significant amount of data set. They would be able to articulate more defined biological implications of Ppg1 function with a thorough data analysis on the valuable resources they already possess. For example, it would be interesting to see how the transcriptome changes in the wild type or ppg1 mutant strains responding to 30 C control vs. 37 C serum conditions. Just for an overview, they would be able to conduct a PCA analysis (or a hierarchical clustering) of 8 samples presented in Table 1 to compare the overall transcriptome of the wild-type and the ppg1 mutant strain under different conditions.

We thank reviewer 2 for all suggestions. We agree with reviewer 2 and provided supplementary file # 217-220 for the 8 conditions presented in Table 1.

2. The authors analyzed the transcriptome of the wild type and the ppg1 mutant strains at 3 and 5 hrs post hyphal induction. Based on their previous study published in 2014, the ppg1 mutant demonstrated a similar expression pattern of ALS3, HWP1, and ECE1 at 3 and 5 hrs. However, the expression of these genes (and NRG1) was quite distinctive at 1 or 2 hrs post-induction compared to the wild-type strain. Thus, it is expected that ppg1 mutation would have impacted the transcription of genes at the earlier stage of hyphal induction. I am wondering why the authors did not choose earlier time points for transcriptome analysis. Is there any clear rationale why the authors chose 3 and 5 hrs only? Of those two time points, the authors only used the transcriptome at 5 hrs for their data analysis and the reason was not clearly stated either.

We agree with reviewer 2 that early time points are important to explore the transcriptional kinetics and the overall effect of PPG1. However, our primary goal in this study was to explore the effect of PPG1 on filament extension, a hallmark of virulence in C. albicans. Previous literature on serum and temperature induction experiments showed a clear transition from pseudohyphae to hyphae around 3 hrs. post induction as explained in method #111-113. We also added figure S3, showing 3 hrs. time points data #229-230.

3. Even though the authors demonstrated that Ppg1 is involved in hyphal induction, it is also possible that Ppg1 would play roles in carbon metabolism under yeast condition. Since the authors already have transcriptome data at 30 C, I would recommend comparing the transcriptome of the wild type and the mutant strains under yeast condition as well.

We agree with reviewer 2 there are multiple combinations of comparisons that can be done, including non-inducing at 30˚C. However, we sought to highlight the most prominent ones (WT vs. mutant at 37˚C + serum at 3 and 5 hrs.). We also added supplementary file # 217-220, as mentioned before.

4. What is the control condition for the metabolomic analysis? Is it the same as the control condition for the transcriptome analysis (30C no serum)? The comparison of the control and inducing conditions were not clearly stated in the method or result sections. Please change "PPG1 null" to "ppg1 delta/delta" to be consistent with the transcriptome data.

We agree with reviewer 2 and have modified the statement in line # 199-202 to reflect that a "Two-way ANOVA was used to compare the strains, treatment and to investigate their interaction and analysis of the metabolite concentration was done to better understand the direction of the changes including the comparison between control and inducing factor as shown in table S3 under the column "serum treatment" and mentioned in results section # 310-311. We have also changed PPG1 null into ppg1Δ/Δ as requested in FIG.3 # 285.

5. Metabolite normalization: if the ppg1 strain is a slower grower, then the final cell number at the harvest time (after 3 and 5 hrs) would have been different. The presentation of the metabolites/total protein would be more appropriate for normalization.

We agree with reviewer 2 and have modified the statements in line # 179-180 "Probabilistic Quotient Normalization [32] normalizes data due to dilution effects in the extraction procedure using the function normalization in the R package KODAMA [33]. We also used Probabilist Quotient Normalization to adjust the different final cell numbers at the harvest time". However, normalizing by protein could be inappropriate since the filaments (made of proteins) could affect protein quantification.

1. In #360-374, the authors claimed that Ppg1 is involved in central carbon metabolism and cell wall architecture. However, the discussion is descriptive and has no supporting evidence. In S. cerevisiae, Ppg1 homolog is involved in glycogen accumulation. Have the authors tested glycogen accumulation in the mutant strain? If Ppg1 plays a role as a master regulator, as the authors speculated, we would expect to see altered cell wall architecture or cell wall integrity. Have the authors tested for cell wall stressor susceptibility or chitin staining to see any compensatory chitin upregulation in the mutant strain?

We have previously shown an important role of PPG1 in call wall adhesion, and the current study confirmed these findings with an insight to the transcriptional and metabonomic contribution toward that observation as shown before in FIG 4 [1]. that said, we agree with reviewers 2 suggestion that other cell wall and biofilm studies are warranted as mentioned # 407.

6. #214 at the 37C non-inducing condition – 30C?

Corrected # 217.

7. #216 can you further elaborate what "comparable results" means?

Corrected # 217-220.

8. In consistent wording for time point description: #218, 5 hours; 3219, 5-hour; #223, 5hr.

Corrected to reflect 5 hrs. wording.

9. #237 and # 238; PPG1 (Gene), Ppg1 for protein

Corrected # 253.

10. #238, "essential genes" are often used for their functional relevance to cell viability. Did you refer the genes that are directly impacted by Ppg1? Then please use a different term.

Agree and corrected # 252.

11. Table 1, could you include functional categories at the front row of this table? Carbon metabolism, hyphal specific genes, etc.

In addition to the gene function category, we can add other categories such as cellular components, biological processes, and Kegg orthology, but we do not feel this is necessary and may overload the table 1.

12. #266 wording in the section title: grown under filament-inducing "condition"

Corrected # 283.

13. #307 "ppg1 mutants" to "ppg1 Δ/Δ"

Corrected # 325.

14. #319 C. albicans to strain, please revise the sentence in #318-320.

Corrected # 336-337.

15. #325 "increased metabolism" can you specify?

Corrected # 343-344.

16. #336 Italicize "Candida"

Corrected # 356.

17. #362 add space between "table" and "1"

Corrected # 382.

18. GEO submission numbers are not found.

Gene expression is provided in the supplimntry file, and the complete data can be provided upon acceptance.

1. Albataineh MT, Lazzell A, Lopez-Ribot JL, Kadosh D. Ppg1, a PP2A-type protein phosphatase, controls filament extension and virulence in Candida albicans. Eukaryot Cell. 2014;13(12):1538-47. Epub 2014/10/19. doi: 10.1128/EC.00199-14. PubMed PMID: 25326520; PubMed Central PMCID: PMCPMC4248689.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

22 Oct 2021

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

PONE-D-21-21871R1

Dear Dr. Al Bataineh,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Additional Editor Comments (optional):

Two original reviewers re-evaluated the revised manuscript, and both agreed that it was properly revised. Although the reviewer 2 made a very minor comment in Figure 3 labeling, I believe that it could be easily fixed during proofreading.

Reviewers' comments:

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: The author addressed my corcerns well, unless some tables inculding Table1 and Table s3 seem incomplete.

Reviewer #2: Just a really minor comment on the Figure 3 data label; please switch the order to be WT control, WT serum, ppg1 null control and ppg1 null serum. Otherwise, the authors addressed the reviewers comments well with clarity.

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Reviewer #1: No

Reviewer #2: No

2 Nov 2021

PONE-D-21-21871R1

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

Dear Dr. AL Bataineh:

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