Waterlogging in soil restricts the growth of Gleditsia sinensis seedlings and inhibits the accumulation of lignans and phenolic acids in thorns

Soil physical and chemical properties

The soil matrix used in this study was divided into two distinct treatment groups: control soil and pindstrup soil. The control soil consisted of a blend of garden soil (purchased from the flower market in Guiyang city) and humus soil (purchased from Organic Fertilizer Development Co., Ltd. in Guiyang city) in a 1:1 (v/v) ratio. The pindstrup soil comprised a mixture of garden soil, humus soil, and Pindstrup moss peat substrate (sourced from Pindstrup Company, Ryomgaard (Kongerslev), Denmark) in a ratio of 0.5:0.5:1 (v/v). Under natural conditions, the pindstrup substrate possesses notable water retention capabilities. To facilitate the experiment, the mixture of soils was then placed in black tree planting bags (25 cm in diameter, 20 cm in height) with free drainage at the bottom. Both types of soil were adequately irrigated, and the soil moisture within the planting bags was monitored within a controlled greenhouse environment. During the measurement process, soil samples were extracted from a depth of 10 cm, with four points sampled and meticulously blended within each planting bag to form one replicate. The soil water potential was promptly determined using a dew point potential meter (WP4-T). Subsequent to the measurement, the fresh soil samples were immediately weighed, dried at 60 °C to a constant weight, and weighed again to determine the dry weight. The soil water content was calculated using the formula: soil water content = (fresh weight−dry weight)/dry weight × 100%. Each treatment was subject to four replicates of soil measurements to ensure statistical robustness.

To examine the disparities in nutrient elements between the two soil types, the nutrient content within each soil was determined. For further analysis, the samples (through a 100-mesh sieve) were digested using microwave-assisted techniques, and the metal element content (such as K, Ca, Mg, Fe, Cu, Zn, Mo, and Mn) was measured using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) following appropriate filtration (Yu et al., 2014). The quantification of metal elements was carried out by referencing the standard curve obtained from the analysis of a mixed standard sample of metals (GNM-M1810581-2013). The total nitrogen (TN) content of the soil was determined through the H₂SO₄–K₂Cr₂O₇ method (Bao, 2020). The total phosphorus (TP) content of the soil was also ascertained employing the molybdenum blue method (Liu et al., 2020).

Seed germination and seedling growth

Mature pods of G. sinensis were meticulously collected from Quanhu Park in Guiyang city, located in Guizhou Province (106°62′E, 26°67′N) and placed in a storage cabinet. Prior to planting, the seeds were subjected to a preparatory treatment. Firstly, they were soaked in concentrated sulfuric acid for duration of 2 h to soften the seed coat (Liu et al., 2023), followed by thorough rinsing with tap water on three separate occasions. Subsequently, the seeds were immersed in tap water at room temperature until they achieved full hydration and experienced notable swelling. The swollen seeds were then planted in the aforementioned tree planting bags, allowing them to grow naturally under ambient conditions (natural rainfall) for a period of one year before commencing with various measurements.

Measurement of seedlings morphological traits

The seedlings were carefully extracted from the planting bags, and the rhizosphere soil was thoroughly rinsed with tap water. Next, the roots were precisely severed at the junction of the roots and stems, and the excess water on the roots was carefully blotted using filter paper. The fresh weight of the roots was recorded, after which the roots were dried at 60 °C to a constant weight, facilitating the determination of the dry weight of the roots. From each seedling, a random selection of four compound leaves was made, and the number of leaflets was accurately recorded. Subsequently, the average number of leaflets was calculated. Additionally, the length of five thorns, located approximately 10 cm from the stem, was meticulously measured from a representative seedling, enabling the determination of the average length of thorns. All the leaves of a single seedling were excised, and the fresh weight of the leaves was measured. Following this, the leaves were dried at 60 °C to a constant weight, facilitating the determination of the dry weight. Furthermore, the lateral branches were pruned from a seedling, and the stem was cut off. The fresh weight of the stem was measured. Subsequently, the stem was dried at 60 °C to a constant weight, allowing for the determination of the dry weight. Four seedlings were used as biological replicates for each treatment.

Determination of nutrient elements in plant tissue

The previously dried plant roots, stems, and leaves were subjected to further processing for the purpose of determining the content of nutrient elements. K, Ca, Mg, Fe, Cu, Zn, Mo, and Mn were quantified using inductively coupled plasma-mass spectrometry (ICP-MS) according to the method of Yu et al., (2014).

Metabolite detection

All of the thorns derived from the stems of a single seedling were meticulously collected. Each experimental treatment comprised 6 seedlings, ensuring an adequate number of biological replicates. Subsequent to thorn collection, they were promptly transferred to centrifuge tubes and rapidly frozen using liquid nitrogen. The frozen thorns were then stored at −80 °C. Approximately 20 mg of the thorn samples were accurately weighed. Subsequently, 1,000 µL of an extraction solution (methanol:acetonitrile:water = 2:2:1(v/v)) containing internal standard mixture was added to each sample. The samples were subjected to grinding at a frequency of 35 Hz for duration of 4 min, followed by sonication for 5 min in an ice bath, with the process repeated three times. After the samples were left at −40 °C for 1 h, they were centrifuged at 12,000 rpm at 4 °C for 15 min. The supernatant was taken and used for detection. An equal amount of the supernatant was mixed to create a quality control (QC) sample, which was used to analyze the repeatability under the same processing method.

The compounds were separated utilizing an Ultra High Performance Liquid Chromatography (UHPLC) (Vanquish; Thermo Fisher Scientific, Waltham, MA, USA) with a Phenomenex Kinetex C18 column (2.1 mm × 50 mm, 2.6 µm). The mobile phase consisted of two components: mobile phase A was a 0.1% acetic acid solution, and mobile phase B was acetonitrile:isopropanol (1:1,v/v). A gradient elution method was employed according to the following time intervals: 0–0.5 min, 1% B; 0.5–4 min, 1%–99% B; 4–4.5 min, 99% B; 4.5–4.55 min, 99%–1% B; 4.55-6 min, 1% B. The column temperature was 25 °C, the injection chamber temperature was 4 °C, the flow rate was 0.3 mL/min, and the injection volume was 2 µL.

Mass Spectrometry (MS) (Orbitrap Exploris 120; Thermo Fisher Scientific, Waltham, MA, USA) combined with control software (Xcalibur, version 4.4; Thermo Fisher Scientific, Waltham, MA, USA) was employed to facilitate the acquisition of both primary and secondary mass spectrometry data. The detailed parameters were as follows: sheath gas flow rate (50 Arb), aux gas flow rate (15 Arb), capillary temperature (320 °C), full MS resolution (60000), MS/MS resolution (15000), collision energy (SNCE 20/30/40), spray voltage (3.8 kV (positive) or −3.4 kV (negative)).

Metabolite identification and data processing

The raw data was processed and converted into mzXML format using the ProteoWizard software. Peak detection and annotation were accomplished through the utilization of an in-house R program and the BiotreeDB database (version 2.1). For multivariate analyses, including orthogonal projections to latent structures-discriminant analysis (OPLS-DA) and principal component analysis (PCA), the MetaboAnalyst 5.0 was employed. In the OPLS-DA analysis, the variable importance in the projection (VIP) value of the first principal component was obtained. Metabolites with a VIP >1 and a P-value < 0.05 (Student’s t-test) were considered to be significantly altered metabolites between the groups. Following the identification of differential metabolites, a subsequent Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was conducted on the differential metabolites.

Statistical analysis

The data were presented as mean ± standard deviation (SD) and assessed for statistical significance using independent sample t-tests conducted in SPSS version 18 (P < 0.05).

Pindstrup substrate maintained waterlogging in soil

The water content and water potential of both soil types were continuously monitored for a period of 16 days (once a day). The findings revealed a decreasing trend in relative water content for both the control soil and the pindstrup soil. However, at a specific monitoring time point (e.g., the 9th day), the water content of the pindstrup soil (53.14%) was significantly higher than that of the control soil (28.36%) (Fig. 1A). In contrast to the results observed for soil water content, the water potential of the pindstrup soil did not display significant changes throughout the 16-day measurement period. Notably, the water potential in the pindstrup soil was found to be significantly higher than that in the control soil (Fig. 1B). These results suggest that the pindstrup soil is capable of maintaining a higher level of available water in the soil matrix, even after a prolonged period without regular irrigation.

Inhibition of seedling growth due to waterlogging

After one year of growth, some morphological traits of the seedlings were measured in both soil types. The results revealed significant differences in plant height, stem fresh weight, root fresh weight, stem dry weight, and root dry weight between the control soil (with measurements of 105.53 cm, 27.63 g, 19.65 g, 10.95 g, and 6.27 g, respectively) and the pindstrup soil (with measurements of 70.66 cm, 13.73 g, 12.10 g, 5.48 g, and 3.38 g, respectively) (P < 0.05) (Fig. 2; Table 1).

Influence of waterlogging on the nutrient content of seedlings

Nutrient elements of the two soils were analyzed to determine whether there was a significant difference between them. The results showed a significantly higher Ca content in the pindstrup soil (31.14 g kg⁻¹) compared to the control soil (13.61 g kg⁻¹). However, no significant differences were observed in the measured nutrient elements content, including TN, TP, K, Mg, Fe, Cu, Zn, Mo, and Mn (Table S1). To further investigate potential differences in nutrient elements in G. sinensis seedlings grown in the two soil types, the contents of nutrient elements in the roots, stems, and leaves of one-year-old seedlings were measured. The results indicated that the contents of Fe and Mn in the roots of seedlings cultivated in the control soil were significantly higher compared to those in the pindstrup soil. Conversely, the contents of Cu and Mo in the roots of seedlings grown in the control soil were significantly lower than those in the pindstrup soil (P < 0.05) (Fig. 3). In the stems of seedlings grown in the control soil, the K content was significantly higher than that in the pindstrup soil, while the Mo content was significantly lower (P < 0.05) (Fig. 3). Furthermore, the Mo content in the leaves of seedlings grown in the control soil was significantly lower than that in the pindstrup soil (P < 0.05) (Fig. 3); however, no significant differences were found in the contents of other elements.

Screening and analysis of differentially abundant metabolites in G. sinensis thorn

Using the UHPLC- MS detection platform, total ion chromatograms (TIC) of thorn samples were obtained under both positive and negative ion modes for the two treatments (Fig. S1). The total ion flow of thorn samples was comparable between the two treatments, but differences in peak area were observed. By matching with the mass spectrometry database (Biotree DB V2.1), a total of 836 metabolites were annotated (Table S2). PCA was performed on all detected metabolites, including quality control samples, to elucidate the variations both between and within treatment groups. The PCA analysis revealed that PC1 and PC2 accounted for 32.1% and 14.6% of the total variation, respectively (Fig. 4A). To obtain more reliable differences, OPLS-DA was utilized to filter the orthogonal variables in metabolites. The OPLS-DA successfully distinguished the metabolites of thorn in the control soil from those in the pindstrup soil (Fig. 4B). These findings indicate significant differences in the metabolites of thorn grown in the two different soil types.

To further explore the disparities in metabolites between the thorn samples from the control soil and pindstrup soil, differentially abundant metabolites were identified. A total of 265 differentially abundant metabolites (VIP >1 and a P-value <0.05) were screened between the thorn of pindstrup soil and control soil, and visualized through a heat map (Fig. 4C). Compared to the control soil, 67 metabolites were up-regulated, while 198 metabolites were down-regulated in the thorn of pindstrup soil (Fig. 4C, Table S3).

To investigate the impact of waterlogging on metabolite accumulation, the detected metabolites were classified into 16 categories (Table 2). Among the down-regulated metabolites, the most abundant category was Fatty acids and derivatives, accounting for 40.58%, followed by lignans (38.71%), phenolic acids (34.48%), saccharides and alcohols (34.15%), steroids (16.67%), alkaloids (12.24%), flavonoids (9.28%), and glycerophospholipids (7.41%) (Table 2). Among the up-regulated metabolites, the most abundant category was nucleotides and derivatives, accounting for 50.00%, followed by glycerophospholipids (18.52%) and steroids (13.89%) (Table 2). Overall, these results also indicated a more pronounced inhibiting effect of waterlogging on the primary metabolites of G. sinensis thorn.

Enrichment analysis of differentially abundant metabolites

To identify the key metabolic pathways affected by waterlogging, the differentially abundant metabolites were matched to the KEGG database for enrichment analysis. Overall, these metabolites were found to be involved in 66 KEGG pathways, including metabolic pathways and biosynthesis of secondary metabolites (Table S4). Among these pathways, metabolic pathways, phenylalanine metabolism, phenylpropanoid biosynthesis, linoleic acid metabolism, tyrosine metabolism, tryptophan metabolism, ABC transporters, citrate cycle, lysine degradation, glyoxylate and dicarboxylate metabolism, cutin, suberine and wax biosynthesis, ubiquinone and other terpenoid-quinone biosynthesis, pyruvate metabolism, sulfur metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis showed significant enrichment (P < 0.05) (Fig. 5). Notably, a significant number of the identified metabolic pathways were associated with amino acid biosynthesis and metabolism, indicating a substantial impact of waterlogging on these pathways.