Background

Dendrobium is an important genus of the Orchidaceae family and one of the largest genera, well-known for medicinal usage. About 1 000 species of Dendrobium are present globally while 74 species and 2 varieties are in China (Wang et al., 2011). D. officinale is the member of Dendrobium, which is a precious traditional Chinese herbal medicine and rich in polysaccharides, amino acids, alkaloids, and other substances, with high medicinal and edible value. Dendrobium can regulate the balance of Yin and Yang in the body, benefit the stomach and promote the production of body fluid (Liang et al., 2019). It has a use in making supplements and functional medicines (Yuan et al., 2020). However, due to over-exploitation and utilization of Dendrobium, its special habitat is highly fragmented and degraded. For instance, D. officinale is usually obtained from artificial herbs to meet the market demand. Artificial planting is often done in greenhouse cultivation and imitates the wild environment and under-forest planting methods (Zuo et al., 2020). However, some problems, such as high temperature, untimely replacement of substrates and improper management, etc., cause the occurrence of germs, pests, and diseases in dendrobium, resulting in the decrease of dendrobium yield and quality (Qiu et al., 2011). It is worth mentioning that Erwinia sp. is one of the most widely distributed pathogens that seriously affect the quality and yield of D. officinale.

The soft rot disease which severely damages the global crops is caused by the gram-negative bacterium Erwinia sp. (Feng, 2017). According to recent reports, soft rot pathogens exist in many cash crops, such as potato tubers (Zimnoch-Guzowska et al., 1999), bell pepper (Hua et al., 2019), taro (Wang et al., 2021), and Dendrobium nobile (Balamurugan et al., 2020). Pathogens mainly enter into the plant through wounds, or natural openings if the conditions are suitable (Hua et al., 2019). After tissues are infected with pathogens, they compete with the host for nutrients to proliferate. At the same time, pathogens secrete a large number of extracellular enzymes, such as pectinase, cellulose, and pectinase to quickly dissolve the plant cell wall and to make the stems and fruits of plants rotten and soft.

Metabolomics approaches have developed rapidly in the last decade and played an important role in understanding plant resistance to biotic and abiotic stress. Thousands of metabolites, such as alkaloid (Razzaq et al., 2019), and flavonoids (Arbona and Gómez-Cadenas, 2015) have been synthesized in the process of plant evolution to adapt to nitrogen (Zhao et al., 2020), phosphorus (Sung et al., 2015) and other abiotic stresses. However, the complex chemical structure and dynamic behavior are the bottlenecks to determining the metabolites (Nikiforova et al., 2018). The development of modern technologies such as gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and nuclear magnetic resonance spectroscopy (NMR) has enabled us to understand the adaptation mechanism of plants under adversity stress (Obata and Fernie, 2012; Razzaq et al., 2019). For example, infrared spectroscopy has been used to analyze the metabolomics of maize growth under different nitrogen nutrient conditions (Pavlík et al., 2010), the metabolite analysis of tomato leaves infected with tomato yellow leaf curl virus (TYLCV) using GC-MS technology (Sade et al., 2014), and the detection using LC-MS technology metabolites of 216 species of Rhizoctonia solani virus infected beans (Mayo-Prieto et al., 2019). These studies play an important role in understanding that crops are subjected to stresses, such as low nitrogen, insect pests, and other stresses.

Metabolomics helps to study the changing process of metabolites during the growth and development, and physiological changes during adversity stress to plants. However, little is known about the effects of Erwinia sp. on the changes of metabolisms in D. officinale. Therefore, metabolomics approaches were adopted to study the Erwinia sp. infection in D. officinale. The aim of this work was to investigate the effect of Erwinia sp. on the metabolic profile in D. officinale, to determine significantly different metabolites between EI and EF plants, and to determine important metabolic pathways of D. officinale in response to Erwinia sp.

1 Results

1.1 Metabolic changes

Our results revealed that 46 and 31 different metabolites were found in EI and EF plants under ESI+ and ESI-, respectively. According to the score plot of PCA, two groups of samples were significantly separated (D. officinale infected with Erwinia sp.: EI; D. officinale free from Erwinia sp.: EF). The results showed that (Figure 1a) the PCA scatter plot of D. officinale plant in ESI+, the PC1 and PC2 showed 51.8% and 11.9% of the total variance, respectively. The PCA scatter plots of the D. officinale plant in ESI- (Figure 1b) presented that PC1 and PC2 explained 43.7% and 13.3% of the total variance, respectively. The metabolomics data was analyzed by OPLS-DA to confirm the differences in the metabolic profiles of EI and EF plants in the positive ion mode. Q2 (cum) was used to estimate predictive power and R2Y (cum) was used to estimate the “goodness of fit” of the model (Jia et al., 2019). OPLS-DA was used to obtain pairwise comparison data. The score plot of OPLS-DA exhibited a significant difference between EI and EF plants under ESI+ conditions (R2Y=0.999; Q2=0.951) (Figure 1c). The significant difference between EI and EF plants in the ESI- condition (R2Y=0.998; Q2=0.928) indicated that the model was stable and reliable with a good predictive ability (Figure 1d). Significantly different metabolites were screened by VIP score (VIP>1) and t-test (P<0.05). The presence of Erwinia sp. may reprogram the metabolism pathway of the host.

1.2 Metabolic profiles of ESI+ and ESI- in response to Erwinia sp.

Under ESI+ condition, Erwinia sp. significantly altered 41 metabolites in D. officinale (Table 1). The presence of Erwinia sp. decreased the content of adenosine (0.51-fold), choline (0.48-fold), L-glutamine (0.59-fold), indole (0.81-fold), benzophenone (0.54-fold), cGMP (0.21-fold), 3,4-dihydroxyphenylglycol (0.12-fold), L-(+)-ergothioneine (0.17-fold), ricinoleic acid (0.15-fold), and phosphoglycolate (0.73-fold) compared to EF. But increased the content of L-histidine (5.97-fold), 2'-deoxyadenosine (3.44-fold), L-phenylalanine (11.14-fold), L-lysine (5.11-fold), betaine (4.45-fold), isoleucine (7.15-fold), D-(+)-tryptophan (6.89-fold), proline (3.70-fold), L-tyrosine 5.92-fold), asparagine (5.05-fold), hypoxanthine (34.70-fold), pipecolic acid (4.94-fold), 4-hydroxybenzaldehyde (6.30-fold), DL-arginine (5.07-fold), crotonic acid (9.69-fold), thymine (9.00-fold), cytosine (2.77-fold), acetophenone (3.99-fold), L-aspartic acid (4.06-fold), L-threonine (3.24-fold), L-(-)-methionine (10.94-fold), guanosine (3.38-fold), DL-2,6-diaminopimelic acid (5.48-fold), alpha-ketoglutaric acid (7.00-fold), D-pantothenic acid (2.47-fold), hexitol (7.07-fold), cytidine (1.96-fold), saccharopine (7.62-fold), pheophorbide A (183.54-fold), niacin (2.49-fold), stearidonic acid (2.64-fold), gamma-aminobutyric acid (9.81-fold), coumarone (4.94-fold), styrene (69.46-fold), uracil (2.37-fold), and toluene (4.83-fold). A total of 46 differentially expressed metabolites in D. officinale were influenced by Erwinia sp. in ESI+, with significant differences observed in the heat map (Figure 2a).

Under the condition of ESI-, 31 metabolites were significantly altered in EI and EF plants (Table 2). The presence of Erwinia sp. decreased the content of lauric acid (0.85-fold), maleic acid (0.60-fold), L-(-)-malic acid (0.62-fold), uridine 5'-diphosphogalactose (0.14-fold), D-glucose 6-phosphate (0.42-fold), 7-methylxanthine (0.53-fold), α,α-trehalose (0.18-fold), theophylline (0.34-fold)3-hydroxy-3-methylglutaric acid (0.44-fold) compared with EF. But increased the content of benzoic acid (4.14-fold), dulcitol (22.75-fold), azelaic acid (1.98-fold), gluconic acid (2.42-fold), guanosine (4.61-fold), thymidine (6.97-fold), L-(+)-lactic acid (17.69-fold), asparagine (10.31-fold), succinic acid (36.88-fold), citric acid (14.70-fold), 16-hydroxyhexadecanoic acid (17.89-fold), 2-oxobutyric acid (2.18-fold), pantothenic acid (3.62-fold), DL-β-leucine (8.36-fold), L-aspartic acid (9.08-fold), DL-tryptophan (17.62-fold), 2-furoic acid (6.52-fold), L-histidine (9.05-fold), 2-oxoglutaric acid (12.34-fold), hexadecanedioic acid (8.26-fold), trans-aconitic acid (12.92-fold), and sulfuric acid (2.21-fold). A total of 31 differentially expressed metabolites of EI and EF plants in ESI-, with significant differences observed in the heat map (Figure 2b). Importantly, there were 4 identical significant metabolites change between EI and EF in ESI+ compared to ESI- by Venn diagram analysis of different metabolites (Figure 3), including Asparagine, Guanosine, L-Aspartic acid, and L-Histidine.

1.3 Effects of Erwinia sp. on the metabolic pathways of D. officinale under ESI+ and ESI- conditions

To investigate the differences in metabolic pathways of EI and EF plants, impact values were estimated by pathway topology analysis and P values by the enrichment analysis. Using MetaboAnalyst 5.0 to analyze metabolic pathways, we found that alanine, aspartate and glutamate metabolism, phenylalanine metabolism, isoquinoline alkaloid biosynthesis, lysine degradation, and lysine biosynthesis might be the most relevant pathways for differential regulation by Erwinia sp. in D. officinale under ESI+ conditions (Figure 4a). Similarly, citrate cycle (TCA cycle), cutin, suberine, and wax biosynthesis, alanine, aspartate, and glutamate metabolism, pyruvate metabolism, starch, and sucrose metabolism, and glyoxylate and dicarboxylate metabolism might be the most relevant pathways responsible for differential expression after Erwinia sp. invasion of stem segments under ESI- conditions (Figure 4b). Thus, Erwinia sp. reprogrammed different physiological processes in the host plants and made the condition of D. officinale worse.

2 Discussion

Dendrobium officinale has a long history of traditional Chinese herbal medicine in China. Previous studies mainly focused on the medicinal components (Cao et al., 2019), seed germination (Tan et al., 2014), and the methods of polysaccharide extraction (He et al., 2018; Liang et al., 2019). However, there are fewer studies on the metabolic changes of D. officinale happen after pathogenic infection. In our study, we used metabolomics techniques to analyze the metabolites of EI and EF plants. After infection, plant metabolism and morphology are greatly altered. Plants may inhibit pathogen growth by producing toxic substances, as well as altering metabolic processes by producing corresponding signaling molecules (Arbona and Gómez-Cadenas, 2015).

After pathogens invasion, the content of amino acids, nucleic acids, alkaloids, and saccharides gradually increase in plants (Parker et al., 2009; Jones et al., 2011). Suharti (2016) et al. demonstrated that parabens increases in Rhizoctonia solani infected rice plants which subsequently triggers programmed cell death in the plant due to pathogen infestation, as a self-defense mechanism of the plant. In addition, p-hydroxybenzoic acid can inhibit the growth of pathogenic bacteria. The increased content of 4-hydroxybenzaldehyde in our study was an effective response to Erwinia sp. disease following infestation. The C metabolism provides energy for N metabolism while N metabolism provides the necessary enzymes for C metabolism in plants (Zhang et al., 2017). Dendrobium officinale infested with Erwinia sp. affected the TCA cycle as well as acetaldehyde and dicarboxylic acid metabolism by increasing the organic acid content of citric acid, cis-aconitate, α-ketoglutarate, and succinic acid in the body. Among them, citric acid was the key metabolite in glyoxylate and dicarboxylic acid metabolism and TCA cycle and was able to produce energy in response to adverse environmental disturbances (Chen et al., 2019). 

In terms of changes in the content of amino acids in plants, the content of lysine, isoleucine, aspartic acid, asparagine, methionine, and proline was significantly increased in EI plants compared to EF plants. Erwinia sp. changed the pathways of alanine, aspartic acid, and glutamic acid metabolism, and phenylalanine metabolism in D. officinale. Proline is a reactive oxygen scavenger that activates various antioxidant enzymes related to the scavenging of reactive oxygen species, protects cells from damage caused by singlet oxygen and hydroxyl radicals, etc., and keeps proteins, DNA, and membrane structures in cells in a stable state (Li et al., 2018). Proline also provides additional compatible osmotic electrolytes to regulate osmotic pressure and to regulate intracellular pH (Miller et al., 2010). Lysine has a regulatory effect on plant photosynthesis, and the EI plant regulates its photosynthesis by altering lysine degradation and biosynthesis metabolism (Lin et al., 2008). The degradation of lysine can produce acetyl coenzyme A, which then enters the tricarboxylic acid cycle (TCA cycle). Moreover, the content of γ-aminobutyric acid, which protects plants from oxidative reactions by regulating lipid acidity, was increased in EI plants (Nikiforova et al., 2018). 

During the metabolism of amino acids, secondary metabolites such as alkaloids and flavonoids are also synthesized (Sumner et al., 2003). The content of betaine was higher in EI than EF plants, which can protect intracellular proteins and metabolic enzymes and maintains enzyme activity as well as cell membrane stability (Goel et al., 2011). Flavonoids are a class of secondary metabolites widely distributed in plants, highly diversified in structure, and involved in many life processes in plants, which mainly play a role in defense against pathogens and protection against stress-induced injury (Arbona and Gómez-Cadenas, 2015). Flavonoids are different in content depending on the functions undertaken by plant organs. For example, when plant leaves are subjected to strong light or UV stress, the leaves synthesize large amounts of flavonoids, which facilitate the absorption of UV light and protect the plant from damage. And bark produces flavonoid phytochemicals to resist damage when it is damaged by microorganisms as well as phytophagous insects (Zhu et al., 2007). EI plants mainly by increasing the content of coumarones, which are precursors of flavonoids and can be transformed by plant metabolism to produce flavonoids and inhibit the enzymes secreted by pathogenic bacteria to degrade the cell wall, thereby protecting the integrity of the plant cell wall (Chai et al., 2017).

It has been shown that sugar can act as an osmotic protectant like amino acids to protect plants under extreme salt, drought, and desiccation stresses (Shulaev et al., 2008). Saccharide not only regulates the osmotic pressure of plant cells but is also a component of plant cell membranes and cell walls (O'Donoghue et al., 2009). Erwinia sp. increases the content of sugars (hexitol, dulcitol) and fatty acids (Stearidonic acid, 16-hydroxyhexadecanoic acid) in the plant to alter the metabolic pathways of cutin, suberine, and wax biosynthesis. Cutin is an important component of the cuticle and plays a vital role in the plant's response to pathogen infection that protects the plant and is also a medium for the exchange of water, gases, and nutrients (Li et al., 2019). In addition, Mahatma (2019) et al. showed that peanuts infested with Alternaria alternate showed increased levels of taxonomic substances, with the phenolics binding to the fungal cell wall and thus making it impermeable to water, thus blocking further growth and the uptake of water and other nutrients by the fungus. Similarly, the amount of coumarone in the D. officinale increases to protect the plant against the disease when Erwinia sp.infests the plant. 

It is worth mentioning that the content of trehalose and glucose 6-phosphate in EI plants in this study was reduced compared to EF plants. Among them, trehalose is the most effective protector of cell membranes, cytoplasm, and proteins. Erwinia sp. mainly reduces the content of α,α-trehalose to reduce the stability of plant cell membranes (Zhao et al., 2020). In addition, glucose 6-phosphate can produce NADH catalyzed by glucose 6-phosphate dehydrogenase, which is used to protect the oxidative balance in plants (Yang et al., 2019). The changes of α,α-trehalose, and glucose 6-phosphate affect the starch and sucrose metabolic pathways. Usually, the α,α-trehalose as well as glucose 6-phosphate content in the body is increased when plants are subjected to pathogens. However, since α,α-trehalose, and glucose 6-phosphate are intermediate products in the metabolic process, it is possible that α,α-trehalose is further hydrolyzed to produce glucose during the metabolic process, which is then oxidized to produce gluconic acid. And glucose 6-phosphate may be broken down through the glycolytic pathway and then enter the TCA cycle process to produce substances such as citric acid. As the samples were collected from one growth time point of D. officinale, there was no continuity for the detection of metabolites, so the α,α-trehalose, and glucose-6 phosphate content of the assay was down-regulated. However, most of the indicative metabolite contents increased in the experiment, therefore, our study suggests that the metabolic pathway was reorganized after Erwinia sp. infecting the D. officinale. Plants improve their disease resistance by consuming the original plant sugars to provide energy to feed other metabolic pathways and by producing the corresponding antimicrobial substances.

3 Materials and Methods

3.1 D. officinale plants and preparation of LC-MS 

Dendrobium officinale were collected from Xicheng Xiushu Agriculture and Forestry base, Anlong County, Qianxinan Prefecture, Guizhou Province in 2019 (105°42′E, 24°99′N). Five EI and six EF plants were randomly selected, carefully transferred to 5 mL centrifuge tubes, and immediately placed in -80°C liquid nitrogen for freezing. A 50 mg of freeze-dried sample was taken in a 5 mL EP tube and extracted with 800 μL of 80% methanol, then vortexed and mixed for 30 s. The mixture was ground to fine powder using Grinding Mill at 60 Hz for 90 s, ultrasonicated for 30 min at 4℃, vortexed and mixed for 3℃. The samples were re-vortexed for 30 s and kept for 30 min at 4℃. Next, they were centrifuged at 12,000 rpm and 4℃ for 15 min, all supernatant was transferred to another 1.5 mL EP tube, kept at -40℃ for 1 h, and again centrifuged at 12,000 rpm for 15 min. Finally, 200 μL of supernatant was added to 5 μL of internal standard (700 μg/mL, DL-o-Chlorophenylalanine), then transferred to a vial for LC-MS analysis.

3.2 LC-MS analysis

Analysis was performed using LC-MS (Thermo, Ultimate 3000LC, Orbitrap Elite), which was based on a Waters ACQUITY UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm). The mobile phase A (95% water + 5% acetonitrile + 0.1% formic acid) and eluent B (98% acetonitrile + 2% water + 0.1% formic acid) with a flow rate of 0.3 mL/min, a column temperature of 40°C, an Automatic injector temperature of 4°C, and an injection volume of 4 μL were employed. The positive and negative ion detection modes of the electrospray ionization source were used (Xu et al., 2020), and the conditions of the electrospray ionization source were set as follows: Electrospray ionization in positive ion mode (ESI+): heater temperature 300°C; sheath gas flow rate: 45 arb; auxiliary gas flow rate: 15 arb; tail gas flow rate: 1 arb; electrospray voltage: 3.0 KV; capillary temperature: 350°C; S-Lens RF Level, 30%. Electrospray ionization in negative ion mode (ESI-): heater temperature 300°C; sheath gas flow rate: 45 arb; auxiliary gas flow rate: 15 arb; exhaust gas flow rate: 1 arb; electrospray voltage: 3.2 KV; capillary temperature: 350°C; S-Lens RF Level, 60%.

3.3 Statistical analysis

Our experiment was performed using SIMCA-P 14.0 software for multivariate statistical analysis, including PCA (Unsupervised principal components analysis) and OPLS-DA (Orthogonal Partial Least Squares-Discriminant Analysis), and the data were also analyzed with independent t-tests, with p<0.05 being considered a statistically significant difference. The VIP is a weighted sum of the OPLS-DA squares (Jia et al., 2019). In addition, differential metabolites were identified by independent samples t-test (p<0.05) and VIP (VIP>1). Subsequently, the metabolites were identified by MetaboAnalyst (http://MetaboAnalyst.ca/) for different metabolites to identify metabolic pathways.

Authors’ Contributions

LJ and WJF are the executor of the experimental design and study; KX, LJ and WJF completed the first draft of the paper; GJY, KX, WJF, WL, CXL, ZP and KM participated in the data analysis and analysis of the experimental results; WJF and YY were the designers and leaders of the project, guiding the experimental design, data analysis, paper writing and revision. All authors read and approved the final manuscript.

Acknowledgments

This research was supported by the Joint Fund of the National Natural Science Foundation of China and the Karst Science Research Center of Guizhou Province (Grant No. U1812401), Guizhou Forestry Scientific Research Project, Qianlinkehe [2022] No. 28, Major Science and Technology Sub-project of Guizhou Science and Technology Program (Qiankehe-2019-3001-2), Fundamental Research Funds for the Central Universities (lzujbky-2021-ey01, lzujbky-2021-kb12) in Lanzhou University; the Open Project of State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University (2021-KF-02).

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