Exogenous glycine and serine promote growth and antifungal activity of Penicillium citrinum W1 from...

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Exogenous glycine and serine promote growth and antifungal activity of

Penicillium citrinum W1 from the Southwest Indian Ocean

Chang-wen Wua*, Xian-liang Zhaoa*, Xiao-jun Wub*, Chao Wenb, Hui Lia, Xin-hua

Chenb**, Xuan-xian Penga**

*These authors contribute equally to this work.

aCenter for Proteomics and Metabolomics, State Key Laboratory of Bio-Control,

MOE Key Lab Aquatic Food Safety, School of Life Sciences, Sun Yat-sen University,

University City, Guangzhou 510006, People’s Republic of China.

bKey Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography,

State Oceanic Administration, Daxue Road 184, Xiamen 361005, P.R. China

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Running title: Glycine promotes antifungal activity.

**Corresponding author: Dr. Xuanxian Peng, E-mail: [email protected];

[email protected]; Dr. Xinhua Chen, E-mail: [email protected]

FEMS Microbiology Letters Advance Access published March 10, 2015 by guest on June 22, 2016

http://femsle.oxfordjournals.org/

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Abstract

PcPAF is a novel antifungal protein identified by our recent study, which is produced

by a fungal strain Penicillium citrinum W1 isolated from a Southwest Indian Ocean

sediment sample. The present study identified glycine as a potential metabolite which

increased the fungal growth and promoted antifungal activity. Then, GC/MS based

metabolomics was used to disclose the metabolic mechanism manipulated by glycine.

With the aid of unsupervised hierarchical clustering analysis (HCA) and supervised

orthogonal partial least squares discriminant analysis (OPLS-DA), the intracellular

metabolite profiles were distinguished among two glycine-treated groups and control.

43 and 47 significantly varied metabolites were detected in 2.5 mM or 5 mM

glycine-treated groups and involved in seven and eight pathways, respectively.

Furthermore, exogenous serine, which is converted from glycine, showed the same

potential as glycine did. Our findings not only identify glycine and serine as nutrients

which promoted P. citrinum W1 growth and increased antifungal activity, but also

highlight the way to utilize metabolomics for an understanding of metabolic

mechanism manipulated by an exogenous compound.

Keywords: Metabolomics; glycine; serine; Penicillium citrinum; growth; antifungal

activity

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Introduction

Microbial products are attractive sources of chemotherapeutic agents and industry

production. Manipulating for the growth conditions of microorganisms and

understanding for the effects of the manipulation are a common strategy used by

pharmaceutical companies to improve yields and diversity of secondary metabolites

of therapeutic interest. Several studies have provided insight into metabolites of

nitrogen source which affect fungal growth and compound production. Trametes

trogii grown in medium with glutamic acid supplement produced the highest laccase

and manganese peroxidase activities (Levin et al., 2010). Urea caused maximum

production of CMCase among the nitrogen sources in Fomitopsis sp. RCK2010

(Deswal et al., 2011). On the other hand, investigations on metabolic regulation are

beneficial for understanding the effects of these manipulations. Hu et. al reported that

arachidonic acid enhanced the maximum β-carotene production in Blakeslea trispora,

which proceeded by increase of the glycolysis and some fatty acids and decrease of

amino acids as a consequence of the arachidonic acid treatment (Hu et al., 2013). Qi

et. al combined metabolomics analysis with cell morphology investigation to improve

rapamycin production in Streptomyces hygroscopicus. Metabolic profiling analysis

showed that most of potential key metabolites were mainly involved in the central

carbon metabolism and nucleotide phosphate metabolism (Qi et al., 2014). Lee et. al.,

compared the mechanism of carotenoids production in three strains during different

growth phases and found that slow the TCA cycle and amino acid metabolism might

promote energy and metabolic flux towards the carotenoids and fatty acids synthesis



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(Lee et al., 2014). These reports suggest that metabolomic profiling is a useful tool to

gain insight into the metabolic pathways and shed light on molecular mechanisms of

elevated production.

Penicillium citrinum is a potential application source of several industrial enzymes,

such as cellulases and lipases, used in detergent and waste treatment applications

(Pimentel et al., 1996; Maliszewska & Mastalerz, 1992). Recently, we have reported a

novel antifungal protein, namely PcPAF, which is produced by a fungal strain P.

citrinum W1 isolated from a Southwest Indian Ocean sediment sample. PcPAF

displays well antifungal activity against Trichoderma viride, Fusarium oxysporum,

Paecilomyces variotii and Alternaria longipes with high thermostability and a certain

extent of resistance to proteases and metal ions (Wen et al., 2014). Improvement of

PcPAF yields is required for the prospective production.

Here, we present an investigation of manipulating growth conditions by exogenous

nutrients and understanding of the manipulation mechanism in P. citrinum W1. A

GC/MS-based metabolomic approach and a multivariate analysis were combined to

investigate the changes in the P. citrinum W1 cell metabolome at different

concentration of glycine treatment. The role of glycine, serine and threonine

metabolism was also confirmed by the subsequent functional investigation using

replacement of glycine with serine. This study provides a better understanding of the

molecular mechanism underlying glycine, serine and threonine metabolism promotes



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growth and antifungal activity of Penicillium citrinum W1.

2. Materials and methods

2.1. Media and culture conditions

P. citrinum W1 was isolated from the Southwest Indian Ocean sediment sample (S

38.1329°, E 48.5975°) (Wen et al., 2014). Seed culture was developed by inoculating

single colony of P. Citrinum W1 into 50 mL Volgel media (pH 7.0) containing 3.0%

sucrose, 0.5% yeast extract, 0.1% K2HPO4, 3.0% NaNO3, 0.5% KCl, 0.5% MgSO4

and 0.01% FeSO4 and incubated on a rotary shaker at 200 rpm for 24 h at 28 0C.

Aliquot 1 mL seed culture was then diluted in 100 mL fresh Volgel medium in 250

mL flasks and grown for 12 days. Samples were collected, centrifuged at 9,000 rpm

for 10 min at 4 0C. Precipitate was used to determine the dry cell mass and metabolite

extract. For nutrition supplement, the following nitrogen sources were used: glycine,

alanine, serine, glutamine, proline, arginine and inosine. All chemicals were

purchased from Sigma-Aldrich.

2.2 Measurement of dry cell weight

To examine the effect of metabolites on dry cell weight, 5 mM metablites including

glycine, alanine, glutamine and inosine were added in Volgel media and grown for 12

days. Growth of P. Citrinum W1 was expressed in terms of dry cell weight (DCW) per

100 mL of culture broth, which was harvested and dried to a constant weight in drying

oven at 60 0C for 48 h and then weighed. Four biological replicates were prepared



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each sample.

2.3 Preparation of metabolome samples

To investigate metabolic response to glycine, two doses of 2.5 and 5 mM were

designated as low- (glycine-L) and high-doses (glycine-H), respectively. Sample

preparation was performed as described previously (Lisec et al., 2006). In brief,

equivalent cells from these cultures were quenched using cold methanol at -40 0C and

collected by centrifugation at 8000 rpm for 5 min. Cellular metabolites were extracted

with 1 mL cold methanol (Sigma) containing 10 μL 0.1 mg/mL ribitol (Sigma) as an

analytical internal standard. Cells were lysed by liquid nitrogen and sonication for 10

min at a 10 W power setting and centrifuged for 10 min at 12,000 rpm at 4 0C. 500 μL

of supernatant was transferred into a new 1.5 mL centrifugation tube and dried in a

rotary vacuum centrifuge device (LABCONCO). The resulting samples were used for

GC/MS analysis. Four biological repeats were prepared each sample.

2.4 Metabolomics analysis

GC/MS analysis. GC/MS analysis was carried out with a variation on the two-stage

technique (Zhao et al., 2014). In brief, for derivatization, 40 μL of 20 mg/mL

methoxyamine hydrochloride (Sigma-Aldrich) in pyridine was added to the dried

samples through a 90 min, 37 0C reaction. Then 80 μL N-methyl-N-(trimethylsilyl)

trifluoroacetamide (MSTFA, Sigma-Aldrich) was added to the samples, and the

mixtures were incubated at 37 0C for 30 min. Chromatography was performed on an



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Trace DSQ II (Thermo Scientific) equipped with a 30 m × 250 μm i.d. × 0.25 μm

DBS-MS column. The derivatized sample of 1 μL was injected with splitless injection

by the Agilent autoinjector. The initial temperature of the GC oven was held at 85 0C

for 5 min followed by an increase to 270 0C at a rate of 15 0C min-1 and then

maintained at 270 0C for 5 min. Helium was used as carrier gas at a constant flow rate

of 1 mL min-1. Electron impact ionization (70eV) at full scan mode (m/z 50-600) at a

rate of 20 scans/s was used. The acceleration voltage was turned on after a solvent

delay of 6 min. Ribitol served as an internal standard to monitor batch reproducibility

and correct for minor variations that occurred during sample preparation and analysis.

Data Processing. Spectral deconvolution and calibration were performed using

AMDIS and internal standards. A retention time (RT) correction was performed for

all the samples, and then the RT was used as reference against which the remaining

spectra were queried and a file containing the abundance information for each

metabolite in all the samples was assembled. Metabolites from the GC/MS spectra

were identified by searching in National Institute of Standards and Technology (NIST

08) Mass Spectral Library. Among the detected peaks of all the chromatograms, 167

peaks were considered as endogenous metabolites excluded internal standard ribitol.

The resulting data matrix was normalized by the concentrations of added internal

standards and the total intensity. This file was then used for subsequent statistical

analyses.



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Statistical analyses. Data transformations and manipulations were done using Excel,

and multivariate statistical analysis was performed with SIMCA-P (Umetrics).

Principal component analysis (PCA) and partial least-squares-discriminant analysis

(PLS-DA) were applied to the data after mean-centering and Ctr-scaling. An

unsupervised PCA was initially performed to obtain an overview of the GC/MS data

from the different groups of the control and glycine-treated P. citrinum cells. Data

were analyzed using SPSS13.0 software for Windows. Differences showing p-values

less than 0.01 were considered statistically significant between control and

glycine-treated groups. Hierarchical clustering was performed on the normalize data,

completed in the R platform with the package gplots

(http://cran.r-project.org/web/packages/gplots/) using the distance matrix. Prior to

analysis, sets of metabolites data subtracted the median metabolites and scaled by the

quartile range in the sample. Z-score analysis scaled each metabolite according to a

reference distribution, and calculated based on the mean and standard deviation of

reference sets control and glycine treatment groups. Detailed accounts of pattern

recognition methods were described by Lindon (Lindon et al., 2001 ).

2.5 Measurement of antifungal activity

The assay for antifungal activity against the tested phytopathogenic Trichoderma

viride was carried out as described previously (Wen et al., 2014). Briefly, one 0.6 cm

diameter piece of Trichoderma viride’ cylinder agar with mycelia growth was placed

on the center of a 90×15 mm petri plates containing 10 mL of PDA medium. After the



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mycelial colony had developed, sterile blank paper discs of 0.65 cm diameter were

placed at a distance of 0.8 cm away from the rim of the growing mycelial colony.

Fifty microliter of culture supernatant collected at each time point was added to each

paper disc. Only culture medium at each time point was used as blank controls. Plates

were incubated at 28 °C until mycelia growth enveloped the control discs and formed

crescents of inhibition around discs containing samples with antifungal activity. The

distance between the inhibited mycelial and the center of paper discs were measured

as the radius of inhibition zone. The experiments were repeated three times. The

radius of inhibition zone was expressed as the mean the standard errors of three

experiments at each time point. The antifungal activity of culture supernatants of P.

citrinum W1 at each time point was characterized by the radius of inhibition zone.

Since antifungal spectrum of culture supernatants of Penicillium citrinum W1 is well

corresponding to that of the antifungal protein PcPAF purified from its culture

supernatants. Thus the increasing production of PcPAF by exogenous glycine/serine

is represented by the increasing antifungal activity （widening inhibition zone radius）

of culture supernatants of P. citrinum W1 at different time points after addition of

glycine/serine.

3. Results

3.1 Effect of exogenous glycine on growth and antifungal activity of P. citrinum

W1

In order to find out which nitrogen source could promote growth and antifungal



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activity of P. citrinum W1, we tested effect of exogenous alanine, glycine, glutamate

or inosine on growth of P. citrinum W1 and found that higher dry weight of the

fungus was detected in the presence of glycine than other sources and control, which

caused a increase by 56% in dry weight (Figure 1A). It was validated by event that

exogenous glycine stimulated growth of P. citrinum W1 in a dose dependent manner

although no significant difference was detected between 5 and 10 mM glycine

addition (Figure 1B). Dynamic growth curve in the form of dry weight from 0 to 12

days showed that 5 mM glycine significantly promoted cell growth (p < 0.01) (Figure

1C). Antifungal activity assay as a consequence of PcPAF yields showed that

inhibitory zone of PcPAF elevated with the increasing exogenous glycine. The

significant effect was detected during 6-12 days for 10 mM glycine and 8-12 days for

2.5 and 5 mM glycine. Exogenous glycine increases the activity by dozens of times in

the eighth days, which saves fermentation period and quarantines high output (Figure

1D). These results indicate that glycine is an ideal nutrient for promotion of P.

citrinum W1 growth and antifungal activity.

3.2 Metabolic profiles of P. citrinum W1 in the presence or absence of glycine

To study metabolic mechanisms by which glycine promote the growth and antifungal

activity of P. citrinum W1, we used GC/MS based metabolomics to investigate

metabolic profile in response to glycine addition. Intracellular metabolites were

extracted and quantified using GC/MS analysis. Typical GC/MS total ion current (TIC)

chromatograms from the control, 2.5 mM or 5 mM glycine-treated P. citrinum W1



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cells were shown in Figure 2A. A total of 167 aligned individual peaks were identified

across glycine addition and control samples. After the removal of internal standard

ribitol and any known artificial peaks and merger of the same compounds, 63

metabolites were identified. Pearson correlation coefficient between two technical

replicates varied between 0.996 and 0.999, which suggests that reproducibility of

GC/MS was sufficient to ensure data quality in global metabolic profiling application

(Figure 2B). Metabolomics approach was used to identify dynamically metabolic

responses to the increasing concentrations of glycine. The metabolite profiles were

displayed as a heat map (Figure 2C), yellow and dark blue indicated increase and

decrease of the metabolites according to the color scale. Orthogonal partial

least-squares discriminant analysis (OPLS-DA), a supervised pattern recognition

method, was carried out for the multivariate analysis. Obvious separation among the

control group and glycine groups in three different quadrants of the PCA scores plot,

and clear separations between 2.5 mM and 5 mM glycine (Figure 2D). In all cases,

94.4% of the total variance in the data was described by the first 2 PCs as R2X=0.944.

This suggests that 94.4% of variation in the data set is attributable to the treatment,

indicating that glycine is likely to have been responsible for the majority of the

metabolic variation in the data.

3.3 Varied metabolomes responsible for glycine and its dose

We further identified differential metabolites between the two glycine groups

(glycine-L and glycine-H) and control using SPSS when p-values less than 0.01 were



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considered statistically significant between control and glycine-treated groups. 43 and

47 significant varied metabolites were obtained in the presence of 2.5 mM or 5 mM

glycine, respectively. For visualizing the relationship among the abundances of

metabolites, hierarchical clustering was used to arrange the metabolites on the basis of

their relative levels across samples (Figure 3A). Z-score displayed variations of these

metabolites based on control. Among the perturbed metabolites, 35 and 31 increased,

8 and 16 decreased in abundance in 2.5 mM and 5 mM glycine groups, respectively.

(Figures 3B and 3C). OPLS-DA was carried out for the multivariate analysis. Obvious

separation among the control group and the two glycine addition groups were detected

in three different quadrants of the PCA scores plot (Figure 3D). Category of these

differential metabolites was compared between the two glycine addition groups.

Number of differential metabolites increased mainly in amino acids and fatty acids

(Figure 3E). Loading plots displayed variables positively correlated with score plots.

The loading plot represented the impact of the significantly differential metabolites on

the clustering results. The metabolites most responsible for the variance were

indicated by their distance from the origin with red mark in the score plot (Figures 3F

and 3G). In the plots of predictive correlation p(corr)[1] and p(corr)[2] (>0.5 or <-0.5),

the red triangles indicate that the differential metabolites have larger weight and

higher relevance. These results indicate that exogenous glycine affects cell

metabolism, which is related to glycine dose.

3.4 Metabolic pathways affected by glycine



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For understanding metabolic pathways affected by glycine, ingenuity network

analysis was used to perform more detailed analysis of pathways and networks

influenced by glycine addition. We analyzed the significantly differential metabolites

to their respective biochemical pathways as outlined in the Kyoto Encyclopedia of

Genes and Genomes (KEGG, release 41.1, http://www.genome.jp/kegg) and MetPA

(http://metpa.metabolomics.ca/MetPA/faces/Home.jsp). Seven and eight pathways

were enriched in the 2.5 and 5 mM glycine groups, respectively. Shared and

differential enriched pathways between them were visualized in Figure 4A.

Specifically, the six overlapping pathways included glycine, serine and threonine

metabolism, galactose metabolism, alanine, aspartate and glutamate metabolism,

cyanoamino metabolism, biosynthesis of unsaturated fatty acids and beta-alanine

metabolism (Figure 4A).

Out of these pathways, glycine, serine and threonine metabolism and cyanoamino

metabolism displays elevated abundance and galactose metabolism shows decreased

abundance in a glycine-dose-dependent manner (Figure 4B), suggesting the metabolic

characteristics in response to different doses of glycine treatment. Furthermore, the

overview of carbon metabolism, amino acid metabolism and fatty acid synthesis

affected by glycine was described in Figure 4C. The results stressed on elevating

abundances of metabolites from glycine, serine and threonine metabolism, alanine,

aspartate and glutamate metabolism, citrate cycle, cyanoamino metabolism and fatty

acid synthesis, while the metabolites of galactose metabolism decreased generally.



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These findings show that increased amino acid metabolism and decreased galactose

metabolism form a characteristic feature in response to exogenous glycine. In addition,

increasing TCA cycle and synthesis of fatty acids are detected although these

pathways are not enriched. These results indicate that glycine promotes growth and

antifungal activity of P. citrinum W1 through modulation of metabolic pathways,

especially glycine, serine and threonine metabolism.

3.5 Effect of exogenous serine on growth and antifungal activity of P. citrinum

W1

To further demonstrate the importance of glycine, serine and threonine metabolism,

replacement of glycine with serine was carried out for testing the effect. The similar

results were obtained when serine was used but not proline or arginine (Figures 5A).

Specifically, dry weight of P. citrinum W1 increased in medium with 5 and 10 mM

serine in comparison with medium without the amino acid (Figure 5A). Antifungal

activity significantly elevated during 6-12 days. Generally, the activity was higher in

medium with 5 or 10 mM serine in six days than in medium without the amino acid in

ten days, suggesting that exogenous serine promotes synthesis of antifungal activity in

advance. Similarly, exogenous serine increase antifungal activity by a dozens of times

in the eighth days in comparison with medium without serine (Figure 5B). Seemingly,

antifungal activity is detected earlier in serine than glycine additions, which may be

related to the flux from glycine to serine (Figures 1D and 5B). These results support

the importance of glycine, serine and threonine metabolism in P. citrinum W1 growth



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and antifungal activity.

4. Discussion

In the present study, we identify glycine as an effective nutrition supplement to

elevate growth and antifungal activity of P. citrinum W1. The demonstration on

elevation of both P. citrinum W1 growth and antifungal activity by glycine leads us to

suppose that the elevation attributes to metabolic modulation. To test this idea, we use

our recently developed functional metabolomics to understand metabolic regulation

mechanisms (Zhao et al., 2014; Guo et al., 2014). In our functional metabolomics

approach, first a crucial biomarker is identified and then the action of the crucial

biomarker is assessed by exogenous addition. Finally, metabolic profile perturbed by

the exogenous metabolite was analyzed for understanding the metabolic regulation

mechanism. Here, following by the demonstration that glycine affects P. citrinum W1

growth and antifungal activity, the combination of GC/MS with multivariate statistical

analyses is used to investigate the most statistically relevant metabolic pathway of P.

citrinum W1 cells as a result of exposure to glycine, which results in identification of

glycine, serine and threonine metabolism as the most affected metabolic pathway that

contributes to the antifungal growth and activity. These results demonstrate the

importance of glycine, serine and threonine metabolism in contributing to P. citrinum

W1 growth and antifungal activity by glycine. Metabolomics has been used for

understanding of metabolic regulation to compound supplements (Hu et al., 2013; Qi

et al., 2014; Lee et al., 2014), but few research groups functionally validate the



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identified pathway. Compared with a single metabolite, pathway gives richer content.

Thus, an understanding of a key pathway is beneficial to know a metabolic regulation.

Glycine is the simplest amino acid in nature and a major constituent in extracellular

structural proteins in animals and microorganism. However, much evidence shows

that the amount of glycine synthesized in vivo is insufficient to meet metabolic

demands (Wu, 2009; Wang et al., 2013). Although mild insufficiency of glycine is not

threatening for life, a chronic shortage may result in suboptimal growth, and other

adverse effects on health and nutrient metabolism (Lewis et al., 2005). The

glycine-dependent metabolic mechanism may be explained from the flux modulated

by exogenous glycine. As shown in Figure 4C, exogenous glycine increases levels of

amino acids and fatty acids and decreases levels of the glycolysis, resulting in

promotion of fatty acid synthesis and the TCA cycle and then contributing to P.

citrinum W1 growth and antifungal activity. Increase flux of fatty acid synthesis and

the TCA cycle are required for fungal growth (Strijbis & Distel, 2010). The finding on

the promotion of exogenous glycine to biosynthesis of fatty acids and the TCA cycle

may be supported by the action of exogenous serine. Glycine is degraded into

pyruvate in two steps. The first step is the reverse of glycine biosynthesis from serine

with serine hydroxymethyl transferase. Serine is then converted to pyruvate by serine

dehydratase. Pyruvate is converted into acetyl-coenzyme A, which is the main input

for a series of reactions known as biosynthesis of fatty acids and the TCA (Strijbis &

Distel, 2010; Hynes & Murray, 2010). Thus, serine and glycine share similar actions.



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5. Conclusion

The present study identifies glycine as an ideal nutritious supplement for

improvement of antigungal activity. For an understanding of mechanisms by which

glycine promotes the activity, a GC/MS-based metabolomics approach is used to

investigate metabolic profile in the supplement. Glycine, serine and threonine

metabolism is determined as the largest impact pathway. Elevated glycine, serine and

threonine metabolism promotes biosynthesis of fatty acids and the TCA cycle, which

have been reported to be pathways contributing to fungal growth. Further the

replacement of glycine with serine demonstrates the reliability of the identified

glycine, serine and threonine metabolism. These results indicate that functional

metabolimcs is a powerful tool for understanding mechanisms manipulated by a

supplement compound.

Acknowledgments

This work was sponsored by grants from Comra fund Grant (D.Y.125-15-T-07),

NSFC projects (30972279, 40976080).

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animals and humans: implications for nutrition and health. Amino Acids 45:

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Figures

Fig. 1 Growth and antifungal activity of P. citrinum W1. A, Dry weight of P.

citrinum W1 in the presence of exogenous 5 mM metabolites indicated. B, Dry weight

of P. citrinum W1 in the presence of glycine indicated. C, Dry weight of P. citrinum

W1 in the indicated time point. D, Antifungal activity in the presence of glycine and

the indicated time point.



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Fig. 2 Metabolic profiles of P. citrinum W1 cultured in Vogel’s medium with

glycine addition. A, Representative total ion current chromatogram from three groups.

B, Reproducibility of metabolomic profiling platform used in the discovery phase.

Abundance of metabolites quantified in samples over two technical replicates is

shown. Pearson correlation coefficient between technical replicates varies between

0.996 and 0.999. This plot shows the two replicates with the weakest correlation of

0.996. C, Heat map of unsupervised hierarchical clustering of variant metabolites

(row). Yellow and dark blue indicate increase and decrease of the metabolites scaled



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to mean and standard deviation of row metabolite level, respectively (see color scale).

D, The score plot of the OPLS-DA model from all detected metabolites. Clear

separations were obtained in the OPLS-DA (two components, R2X=0.942,

R2Y=0.974, Q2=0.944) scores plots derived from standard GC-MS data.



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Fig. 3 Differential metabolomic profiling of P. citrinum W1 in response to

exogenous glycine. A, Heat map of unsupervised hierarchical clustering of

differential metabolites (row). Glycine-L (2.5 mM glycine), glycine-H (5.0 mM group)

Yellow and dark blue indicate increase and decrease of the metabolites scaled to mean



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and standard deviation of row metabolite level, respectively (see color scale). B, The

score plot of the OPLS-DA (supervised orthogonal partial least squares discriminant

analysis) model from variant metabolites. B-C, z-score scatter diagrams of differential

metabolites based on control. The data from tested groups are separately scaled to the

mean and standard deviation of control. Each point represents one metabolite in one

technical repeat and is colored by sample types. D, Number of metabolites increased

and decreased in different categories. E-G, The distribution of differential abundance

of metabolites’ weight from method of OPLS-DA to control and experimental

samples. Triangles in metabolites’ scatter diagram marked with color own larger

weight and higher relevance.



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Fig. 4 Features of differential metabolites. A, Pathway enrichment analysis of

differential metabolites. Significant enriched pathways are selected to plot. B, The



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change of common metabolic pathway. C, Common metabolic pathways of

significantly changed metabolites in two tested groups based on control group. Red

and green indicate increase and decrease of the metabolites, respectively. Black and

gray indicate no change and no detection in GC-MS tests, respectively.



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Fig. 5 Action of serine in growth of P. citrinum W1 and antifungal activity. A, dry

weight of P. citrinum W1 in the presence of exogenous metabolites indicated. B,

Antifungal activity in the presence of glycine and in the indicated time point.



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