Metabolomics Analysis of Combretum lanceolatum Roots in the Presence of Its Endophytic Fungi

Abstract

The aim of this work was to identify and quantify the metabolites present in the roots of Combretum lanceolatum inoculated with its endophytic fungi. The metabolomics was accomplished using the ¹H nuclear magnetic resonance (NMR) spectral data and evaluated via rNMR software and Madison Metabolomics Consortium Database (MMCD). The principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) showed that plants inoculated with Trichoderma spirale (Ts) present differentiation and discrimination over the time compared to control. Seven days after Ts fungal inoculation, 15 metabolites were identified at different concentrations comparing to the control plants. The plants inoculated with Ts fungus present the metabolites spermidine and pantothenate in higher concentrations and 3-hydroxybutyric acid and b-alanine in lower concentrations compared to control plants, indicating any response to biotic stress. These metabolites are involved in various plant processes, including secondary metabolites biosynthesis, energy metabolism and self-defense. Therefore, this work demonstrates the diversification of primary metabolites composition influenced by endophytes inoculation.

Keywords:
plant-fungus interaction; chemical profile; primary metabolism; Trichoderma spirale; Diaporthe phaseolorum

Introduction

The plant-fungus symbiosis is a harmonic type relationship established between plants and endophytes present within the tissues, especially in the roots of plants.¹ Endophyte plays an asymptomatic relationship with the host plant, leading both morphophysiological and biochemical changes such as the biosynthesis of the primary and secondary metabolites, which improves the microbiota development and resistance of the plant to biotic and abiotic stresses.²

The chemical investigation of plant-fungi relationship has been described on several occasions, such as the auxin signaling role of Trichoderma virens in plant growth regulation,³ the increase of aroma compounds in Aquilaria spp plants associated with Fusarium fungi⁴ and the Rhizophagus irregularis colonization of Senecio jacobaea plants, which caused significant changes in the root metabolome by increasing the chemical defenses.⁵ In this context, the study of a plant-fungus relationship, especially on their metabolomics profile, represents an interesting and relevant research area to understand their mutualistic symbiosis and its contribution to plant development.⁶-¹⁰

Among analytical methods employed on metabolomics studies,¹¹,¹² the hydrogen nuclear magnetic resonance (¹H NMR) technique has been used to study metabolomes because it allows the detection and quantification of primary and secondary metabolites.¹³ Recently, the ¹H NMR technique has been used for plant and fungus metabolome approach, such as the variation of metabolites involved in carbon metabolism of soybean plants infected by Sclerotinia sclerotiorum,¹⁴ the effects of the flavonoids genistein, daidzein, apigenin, and kaempferol on mycotoxin citrinin production by Monascus purple-red molds,¹⁵ the influence of environmental conditions on the metabolome of Solanum lycopersicum¹⁶ and the chemical profile analysis of ten different Capsicum annuum cv. serrano plants cultivated in Mexico.¹⁷

Combretum lanceolatum is an endemic plant occurring in wetland areas of South America, which has demonstrated relevant biological activities such as antioxidant and anti-hyperglycemic.¹⁸-²⁰ Although C. lanceolatum plant has been studied regarding its bioactivity properties, only a little attention has been given towards its phytochemical investigation in the literature.²¹,²² In a previous work,²² our research group described the metabolomic changes in the aerial parts of C. lanceolatum plants inoculated with its endophytic fungi Diaporthe phaseolorum (Dp) and Trichoderma spirale (Ts). Accordingly, the study demonstrated that Dp fungus has significantly affected the plant aerial parts metabolic pathways, improving the biosynthesis of primary metabolites such as threonine, malic acid and N-acetyl-mannosamine.²² Based on those results, the aim of our current work is to analyze the effects of Dp and Ts fungi on the chemical composition of C. lanceolatum roots in order to extend the metabolomics knowledge over the symbiotic interaction between these species.

Experimental

Plant cultivation and endophytic fungal inoculation

C. lanceolatum seeds were collected from Pocone, Mato Grosso state of Brazil, coordinates 16°18’93.2” S and 56°32’29.1” W. A voucher specimen was deposited in herbarium of the Federal University of Mato Grosso (code No. 39.149). The endophytic fungi Dp (No. of Access GenBank KF555229) and Ts (No. of Access GenBank KF555225) were isolated from the roots of C. lanceolatum.²³

The C. lanceolatum seeds disinfection, germination and cultivation as well as the endophytic fungi Dp and Ts inoculation were performed as previously described.²² The experiment was divided into three fungal interaction times, immediately after inoculation (0 days), after 1 day and 7 days. For each fungal interaction time, the uninoculated C. lanceolatum plant was used as a negative control. The experiment was performed in triplicate manner.

Plant material extraction

Each root sample was frozen under liquid nitrogen, macerated to a fine powder using mortar and pestle, followed by solvent extraction with ethanoic aqueous solution (50% v/v, 4 mL) with formic acid (1%). The extraction procedure was performed employing a Marconi vortex mixer (Piracicaba, SP, Brazil) at 1600 rpm for 30 min and repeated three times for each sample. The extract solutions were filtered with 0.45 µm filters (Millipore, MA, USA) and the solvent was removed under reduced pressure followed by lyophilization. All dry extracts were collected and stored at -80 °C in a Thermo Scientific refrigerator (Long Branch, USA).

¹H NMR data acquisition and spectra processing

The ¹H NMR analysis was conducted at 4.0 mg mL^-1 of vegetal material per treatment, employing as a solvent deuterium oxide with 0.05 wt.% of sodium 3-(trimethylsilyl) propionate-2,2,3,3-d₄ (TSP) purchased from Sigma-Aldrich (St. Gallen, Switzerland). The TSP was used as both a chemical shift reference (H_d 0 ppm) and internal standard for quantitative analysis.²⁴,²⁵ After the solution preparation, samples were taken in the NMR tubes (5 mm) and immediately submitted for ¹H NMR spectra acquisition using a Bruker Magnet System AscendTM spectrometer (Fällanden, Switzerland) at 500 MHz, equipped with a 5 mm broadband observe probe.

The ¹H NMR spectra were acquired at 25 ºC with 128 transients of 64 K data points, 90° pulse angle, 4.35 s acquisition time and 4 s recycle delay.²²,²⁶ Using a general procedure,²⁷ 1H NMR spectra pre-processing and multivariate analyses of data for metabolite identification were performed with a few modifications. The spectra were phased and the baseline were corrected using MestReNova software.²⁸

Identification and quantification of chemical profile

The NMR data were processed with rNMR software²⁹ in order to normalize the spectra (with the total area of each spectrum) and generate the spectral matrix,³⁰ followed by cross-reference analysis employing Madison Metabolomics Consortium Database (MMCD).³¹,³²

Statistical analyses

The statistical analyses were performed using the metabolites data obtained through rNMR and MMCD. All multivariate analysis (mean-centering and scaling principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA)), Vulcano plot (threshold limit of P < 0.05), unpaired T-test and Fold Change (FC > 1.0) were performed with MetaboAnalyst 4.0 online platform.³³,³⁴

Results and Discussion

Metabolome profiling in plant-fungal association by ¹H NMR

The effects of Dp and Ts fungal synergism on the metabolomic profile of C. lanceolatum plants were evaluated after 0, 1 and 7 days of fungal inoculation. The most relevant results were obtained on Ts-plant experiment after 7 days of interaction, indicating the presence of Ts fungus potentially affected the plant metabolism as compared to the plant control. Additionally, the association of Dp fungus to C. lanceolatum plant showed no variation in metabolomic profile over the experiment time as compared to the control.

Representative ¹H NMR spectra acquired from hydroalcoholic extracts of plant-Ts fungi interaction (orange) and plant control plant (gray) after 7 days of interaction are shown in Figure 1. The analysis of ¹H NMR spectral data set using rNMR software and MMCD lead to the identification and quantification of 105 primary metabolites (see Supplementary Information section).

Figure 1
Representative 1D ¹H NMR spectra of hydroalcoholic extracts of plant-Ts fungi interaction (orange) and plant control plant (gray) after 7 days of interaction. Aromatic region (a), carbohydrate region (b) and aliphatic region (c).

The identified metabolites include carbohydrates (threitol, adonitol, fructose-6-phosphate and sorbose), glycerol, ethanolamine and N-acetyl-neuramic acid, detected at the chemical shift average 3.00 to 4.50 ppm (Figure 1b). In the aliphatic region (Figure 1c), ¹H NMR spectrum of the plant-Ts treated samples demonstrated differences compared to the plant control, related to the compounds methionine, glutathione and spermidine. Although the signals were observed with lower intensity in the aromatic region (Figure 1a) compared with those in the carbohydrate (H_d 5.50-3.00 ppm) and aliphatic (H_d 3.00-0.50 ppm) regions, formaldehyde was presented in higher concentrations in inoculated plants as compared to the control.

Furthermore, the fungal association induces different metabolomic variations towards the plant aerial parts and roots, since the interaction of Dp fungus with C. lanceolatum plants significantly affected the metabolic profile, increasing the primary metabolites such as threonine, malic acid and N-acetyl-mannosamine.²² Based on these results, the Ts fungus interaction represents a more relevant influence on the biosynthesis of primary compounds in the plant roots, improving the plant’s development and secondary metabolism pathways.

Statistical multivariate analysis

The ¹H NMR spectral data (chemical shift and intensity) were used as a matrix in the multivariate analyses. The principal component analysis (PCA) was used to analyze the NMR data set and loading plots were used to detect the spectral areas (metabolites) responsible for separation in the data.³⁵ Data set obtained from the analysis of plant-Ts fungi interaction after 7 days of inoculation, the first two components model could explain 74.9%. Among the PCs, combination of PC1 and PC2 can give well-separated two clusters for plant-Ts fungi interaction as compared to the control plants (Figure 2a).

Figure 2
Scores plots of PCA (a) and PLS-DA (b) of the hydroalcoholic extracts of plant-Ts fungi interaction (green) and plant control plants (red) after 7 days of interaction.

The multivariate analysis of spectral data proved to be a useful tool in order to discriminate between treated plants (with Dp and Ts fungi) and control plants. From the PCA score plot, it is obvious that Ts fungus induced metabolic perturbations in C. lanceolatum roots after 7 days of inoculation. The samples originating from the Ts-fungi interaction and control plants could be observed as clusters in different areas of the plots. The first component, which explains 42.4% of the total variance, includes the metabolites 1,4-diaminebutane, muco-inositol and trimethylamine. At the opposite position on PC1, we can find the metabolites 6-phosphogluconic acid, glyceraldehyde-3-phosphate and choline.

To investigate the effects of plant-fungi interaction over the experimental time, partial least squares-discriminant analysis (PLS-DA) models were performed employing two components to demonstrate metabolic differences between the plant control and Ts-fungi interaction groups. The score plot of the two firstly components (PC1 and PC2) for PLS DA model of plant-Ts fungi interaction and plant control after 7 days of interaction showed separation of the classes, mainly in the first component (Figure 2b). The PC1 and PC2 explain the total variance with 71.2% contribution and Q² parameter obtained was 0.68 (R²X = 0.98, R²Y = 0.99). The control and Ts fungi interaction groups clustered on positive and negative sides, respectively, along the component 1 axis. The PLS-DA loadings were analyzed and the compounds that affected the metabolic profile variation were identified. The most significant metabolic variation found between control and Ts fungi interaction extracts included 3- hydroxybutyric acid, trimethylamine and alanine for the control group and glycolate, 6-phosphogluconic acid and sorbitol for Ts fungi interaction.

Furthermore, the differences observed between the metabolomic profiles of Ts fungi interaction and plant control are due to the entire metabolome and not due to the major contribution of a few differentially abundant metabolites. Considering these metabolites are involved in many biosynthetic pathways, including the secondary metabolite synthesis, the variations observed on primary metabolites concentration through endophytic fungal association potentially influence the biosynthesis of important compounds for plant development.³⁶

Identification of significant metabolites and determination of fold changes

Aiming to identify the primary metabolites that are present at different concentration in comparison with fungal inoculated plants (FIP) and control plants (CP), fold changes (log₂(FC)) of the concentration ratio of FIP by CP and related p values were determined for each treatment. The calculations of fold changes and T-tests were performed with MetaboAnalyst. When comparing Ts inoculated plants to control plant samples, a total of 27 ions presented significant accumulation differences (p ≤ 0.05 and fold change ≥ 1.0) over the interaction time (0, 1 and 7 days), of which 12 metabolites presented an increase, and 15 metabolites a decrease, in their accumulation (Table 1). Volcano plot analyses for the metabolomic profile of C. lanceolatum-Dp interaction were not performed since the group discrimination were not observed in multivariate analysis.

Table 1
Identified metabolites with differential accumulation in Ts-plant association with its fold changes (p ≤ 0.05, log₂ (FC) ≥ 1.0 or ≤ -1) over interaction time

Metabolite	Interaction time
	0 day			1 day			7 days
	log2(FC)	p-Value		log2(FC)	p-Value		log2(FC)	p-Value
2,3-Diphosphoglyceric acid	2.77	0.001		-	-		-	-
Sorbose	1.23	0.041		-	-		-	-
Threitol	1.11	0.023		-	-		-	-
α-Galactose-1-phosphate	-1.02	0.030		-	-		-	-
Fructose	-1.20	0.043		-	-		-	-
Cysteine	-1.42	0.037		-	-		-	-
Galactono-1,4-lactone	-	-		1.08	0.049		-	-
α-Lactose				1.67	0.046		-	-
Arabinose	-	-		-1.18	0.010		-	-
Pyruvate	-	-		-1.14	0.032		-	-
Succinate	-	-		-1.14	0.033		-	-
N-Carbamoyl-methyl-imino-diacetic acid	-	-		-1.12	0.036		-	-
Spermidine	-	-		-	-		2.95	4.1 × 10-4
Pantothenate	-	-		-	-		2.55	0.008
Glyceraldehyde-3-phosphate	-	-		-	-		1.79	0.027
Threitol	-	-		-	-		1.60	0.001
Serine	-	-		-	-		1.59	0.012
Formaldehyde	-	-		-	-		1.56	0.004
Sorbitol	-	-		-	-		1.06	5.1 × 10-4
Acetic acid	-	-		-	-		-1.05	0.006
Saccharate	-	-		-	-		-1.21	7.4 × 10-4
meso-Erythritol	-	-		-	-		-1.26	0.001
Glutathione oxidized	-	-		-	-		-1.36	0.003
Trimethylamine	-	-		-	-		-1.38	7.4 × 10-4
1,4-Diaminobutane	-	-		-	-		-1.82	0.004
β-Alanine	-	-		-	-		-4.31	5.0 × 10-5
3-Hydroxybutyric acid	-	-		-	-		-4.40	0.0002

At the initial interaction time of the C. lanceolatum-Ts experiment, there was observed modification on six metabolites concentrations. Based on the fold changes results, 2,3-diphosphoglyceric acid (log₂(FC) = 2.77), sorbose (log₂(FC) = 1.23) and threitol (log₂(FC) = 1.11) were present at higher concentrations in FIP than in CP. These metabolites are related to energetic sugar metabolism, directly involved in the glycolysis pathway through 2,3-diphosphoglyceric acid,³⁷ being extended to sorbose and threitol, which are natural sugars.³⁸,³⁹ Cysteine (log₂(FC) = -1.42) and the carbohydrates fructose (log₂(FC) = -1.20) and a-galactose-1-phosphate (log₂(FC) = -1.02) were present at lower concentration in FIP than in CP, indicating the reduction of cysteine concentration potentially be related to the synthesis of methionine and glutathione.⁴⁰

After one day, plants were inoculated with Ts fungus, a-lactose (log₂(FC) = 1.67) and galactono-1,4-lactone (log₂(FC) = 1.08) were present at higher concentrations in plant-Ts treatment group. Galactono-1,4-lactone is an intermediate compound in the galactose pathway, that is the main route towards galactose catabolism and ascorbic acid formation.⁴¹ The galactose catabolism may be induced in the interaction between endophyte and plant, which explains the reduced level of arabinose (log₂(FC) = -1.18), which is quickly consumed in the presence of galactose or lactose according to the Leloir pathway.⁴² Furthermore, pyruvate (log₂(FC) = -1.14) and succinate (log₂(FC) = -1.14) present at lower concentration in FIP than in CP. These compounds are important metabolites in the glycolytic pathway in plants or fungi metabolism and are also related to acetate and mevalonate pathways for the biosynthesis of polyketides, fatty acids, terpenoids or steroids, as well as to biosynthesis of aliphatic amino acids, building blocks of proteins or secondary metabolites, such as alkaloids.⁴³-⁴⁶

The diversity of chemical components resulting from the interaction of plant fungi has increased over time. With the increase in reaction time, there were observed more metabolites in the extract of the plant inoculated with Ts fungus. After 7 days of inoculation, 15 compounds were detected as changes in the metabolic profile. Spermidine (log₂(FC) = 2.95) and pantothenate (log₂(FC) = 2.55) were present at significantly higher concentration in FIP than in CP after 7 days of plant-Ts interaction. Spermidine is a polyamine compound derived from 1,4-diaminobutane (log₂(FC) = -1.82) that is related to abiotic stress relieve⁴⁷ and increase of antioxidant proprieties, phytohormone levels and adenosine triphosphate (ATP) concentration, which represents a parameter for energy metabolism quantification in plants and fungus.⁴⁸ In addition, serine was detected at a higher concentration in the treated plants (log₂(FC) = 1.59). This amino acid is involved in the glycine-betaine metabolism and the glyoxylate pathway, but the main route for its formation is by 3-phosphoglycerate transformation.⁴⁹ It has multiple functions in osmoprotectant and as a building block of several other metabolites such as tryptophan, glutathione, porphyrins and fatty acids, which play an important role in defense, anabolic action and energy metabolism.⁵⁰

According to pantothenate levels increasing, there was observed a significant reduction of b-alanine (log₂(FC) = -4.31) and 3-hydroxybutyric acid (log₂(FC) = -4.40) concentrations. Plants and fungi produce pantothenate through the condensation of b-alanine with pantoate, which is obtained in two steps from an intermediate in branched-chain amino acid biosynthesis. Pantothenate is the main precursor of coenzyme A (CoA),⁵¹ which is involved in innumerable reactions of central metabolism, as fatty acid oxidation, and biosynthesis of glycolipids and sterols, as well as secondary metabolic pathways, including those for polyketides, non-ribosomal protein synthesis, flavonoids, and lignin.⁵²,⁵³ In plants, 3-hydroxybutyric acid acts as a regulatory molecule that most likely influences the expression of genes involved in deoxyribonucleic acid (DNA) methylation.⁵⁴ These results indicate that the plant-fungus interaction potentially modify the biosynthesis of secondary metabolites.

Glyceraldehyde-3-phosphate present higher concentration in FIP (log₂(FC) = 1.79) over 7 days of interaction. This metabolite is an intermediate of glycolysis and gluconeogenesis and also the precursor for deoxyxylulose phosphate pathway, which leads to the formation of terpenoid and steroid metabolites.⁵⁵ Moreover, there were observed an increase levels of threitol (log₂(FC) = 1.60) and sorbitol (log₂(FC) = 1.06) and reduction of meso-erythritol (log₂(FC) = -1.26), which metabolites are related to energy metabolism, carbon source and signaling molecules in plants.⁵⁶

Therefore, the differences observed in the metabolomic profile of C. lanceolatum inoculated with Ts compared to uninoculated plants demonstrated the variations on biochemical pathways, inducing or inhibiting the synthesis of some primary metabolites as carbohydrates and amino acids.

Conclusions

In conclusion, this work demonstrate the metabolomic profiling of C. lanceolatum plant roots inoculated with its endophytic fungi. The metabolomic approach was based on ¹H NMR spectral data and analysis performed employing rNMR software and MMCD database. Multivariate analysis showed that roots inoculated with Ts fungus presents significantly class differentiation and discrimination over 7 days of interaction as compared to roots of control, indicating the chemical profile of the plant-fungus association is variable and time-dependent. Based on the fold change analysis, it was observed that Ts fungus affected the metabolic profile of the roots by inhibiting or improving the biosynthesis of primary metabolites such as spermidine, pantothenate, b-alanine and 3-hydroxybutyric acid, which are involved in various plant processes such as secondary metabolites biosynthesis, energy metabolism, carbon source and self-defense. Therefore, we demonstrated that the interaction between roots and endophytic fungi results in the plant’s metabolomic profile changes, which suggest the influence of fungal association for plant development.

Supplementary Information

Supplementary information (multivariate analysis and table of compounds annotation and concentration) is available free of charge at http://jbcs.sbq.org.br as PDF file.

Acknowledgments

We gratefully acknowledge FAPEMAT (grant 0250685/2017) and UFMT for financial support and fellowships. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

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Edited by

Editor handled this article: Paulo Cezar Vieira

Publication Dates

• Publication in this collection
  10 Feb 2023
• Date of issue
  2023

History

• Received
  17 Apr 2022
• Accepted
  25 July 2022