The tissue culture seedlings of Sophora tonkinensis used in this study were sourced from the in vitro conservation collection at the Medicinal Botanic Garden. These specimens were derived from wild populations collected in the Jingxi Mountainous Area of Guangxi Zhuang Autonomous Region in 2016. The voucher specimen of S. tonkinensis is housed within the Conservation Center of the Botanic Garden, with the specimen number JX00763. Species identification was carried out by the corresponding author, Wei Kunhua (Tables 1, 2).
Mg treatment and experimental setup
Tissue culture seedlings of S. tonkinensis were selected from the in vitro library. Stem segments, approximately 2–3 cm in length, were obtained from these seedlings and propagated in a new medium (MS + 5.0 mg L− 1 6-BA + 0.3 mg L− 1 IAA + 0.3 mg L− 1 KT). Once the explants produced numerous clustered buds, regenerated buds were transferred to a robust seedling culture medium (MS + 1.0 mg L− 1 IAA) for further cultivation. After the tissue culture seedlings had lignified sufficiently, those with a high lignification degree were chosen. The top stem segments, including leaves and measuring 2–3 cm in length, were selected and inoculated into media with varying magnesium ion concentrations for rooting. Each bottle contained 8 explants, and 30 bottles per treatment were used. Six treatments were designated as T0, T0.5, T1, T2, T3, and T4. Building on existing methods for regulating magnesium levels in tissue culture experiments23,24, this study employs varying concentrations of magnesium sulfate to modulate magnesium levels. The magnesium concentration for each treatment is listed in Table 3, and the experiment extended over 60 days. The medium for root culture was supplemented with sucrose (final concentration 30 g L− 1) in addition to the culture medium for promoting the strong seedling. The medium contained 3.4 g/L of agar with a pH = 5.8. After inoculation, the cultures were maintained at a temperature of 25 ℃, with a light intensity of 1500–2000 lx and 12-hours’ light per day. After 60 days of rooting culture, the materials were collected for phenotype determination, biochemical analyses, and omics data collection.
Phenotype determination
Collect materials and measure various parameters of tissue-cultured seedlings, including plant height, stem diameter, leaf count, rooting rate, root length, and root dry weight.
Plant Height: Measure the maximum height of the main stem using a ruler.
Stem Diameter: Determine the diameter of the lower stem with a vernier caliper.
Leaf Count: Count the number of proliferating leaves.
Rooting Rate: Calculate the rooting rate as the number of rooting explants divided by the number of inoculated explants, multiplied by 100%.
Root Length: Measure root length with a ruler.
Root Dry Weight: After collecting S. tonkinensis roots, clean them with water, and then oven-dry until a constant weight is reached. Weigh the roots.
Determination of active ingredient contentDetermination of matrine and oxymatrine
The methods for measuring the matrine and oxymatrine content followed the Pharmacopoeia of the People’s Republic of China version 2020 .
Extraction Method: 0.1 g of the sample was mixed with 2 mL of the extraction solution (methylene chloride: methanol: ammonia solution in a ratio of 40:10:1). The mixture was kept at room temperature for 30 min, followed by 30 min of sonication. Afterward, it was centrifuged at 4000 rpm for 10 min at room temperature. After centrifugation, 1 mL of the resulting supernatant was dried under nitrogen at 40 °C, followed by reconstitution with 1 mL of methanol. The solution was subsequently filtered through a 0.22 μm syringe filter before analysis.HPLC conditions: HPLC equipment – Shimadzu LC-2030 Plus(Shimadzu, Kyoto, Japan). The Chromatographic column – Agilent Polaris 5 NH2 (5 μm, 250 × 4.6 mm). The mobile phase consisted of acetonitrile-isopropanol-3% phosphoric acid solution (in a ratio of 80:5:15). The injection volume was 5 µL with a flow rate was 0.5 mL/min, and the column temperature was maintained at 25 °C. Detection was performed at a wavelength of 210 nm.
Determination of maackiain and trifolirhizin
Extraction Method: Approximately 0.1 g of the sample was weighed and mixed with 1 mL of methanol, followed by thorough grinding. The mixture was then subjected to 50 min of ultrasonication. The volume was adjusted to its original size using methanol. After centrifugation at 4000 rpm for 10 min, the supernatant was collected and filtered through a 0.22 μm membrane.
HPLC Conditions: HPLC equipment – Shimadzu LC-2030 Plus(Shimadzu, Kyoto, Japan). (Shimadzu, Kyoto, Japan). The Chromatographic column – Agilent Plus C18 column (5 μm, 250 × 4.6 mm). The mobile phase consisted of acetonitrile and water with a gradient program as follows: The gradient program consisted of the following stages: starting with 25% acetonitrile for 5 min, it was followed by a gradual increase to 50% acetonitrile for 50 min, then a further rise to 95% acetonitrile for 25 min, maintaining 95% acetonitrile for 6 min, returning to the initial 25% acetonitrile for 1 min, and finally equilibrating at this concentration for 5 min. Detection occurred at a wavelength of 205 nm, using a 10 µL injection volume, a flow rate of 1 mL/min, and a column temperature of 30 °C. Detection was carried out at a wavelength of 205 nm. The injection volume was 10 µL, the flow rate was 1 mL/min, and the column temperature was set at 30 °C.
Collecting omics data
Given that only the T2 treatment was able to elevate the content of maackiaian, it was chosen alongside the T0 treatment for the collection of omics data.Each treatment has three replicates. Moreover, the samples were also used for transcriptomic sequencing and proteomic data collection.
Transcriptomic data collection and analysis
Materials were frozen using liquid nitrogen and then transported to the MajorBio (Shanghai, China) for transcriptomesequencing. Transcriptome sequencing was performed using the Illumina Novaseq 6000 platform (San Diego, USA).
The raw paired end reads were processed using fastp25 with default parameters. Then clean reads were separately aligned to the reference genome of S. tonkinensis with orientation mode using HISAT225 software. The transcriptome reference genome data for S.tonkinensis (unpublished) was supplied by the Botanical Garden. The mapped reads for each sample were assembled by StringTie25 in a reference-based approach.
To identify DEGs (differential expression genes), the expression level of each transcript was calculated by the transcripts per million reads (TPM) using RSEM25. Essentially, differential expression analysis was performed using the DESeq226. DEGs with |log2FC|≧1 and FDR ≤ 0.05(DESeq2 ) were considered to be significantly different expressed genes. Furthermore, functional enrichment analysis, incorporating GO and KEGG, was conducted to pinpoint the DEGs significantly enriched in GO terms and metabolic pathways, with a Bonferroni-corrected P-value threshold of ≤ 0.05 compared to the entire transcriptome dataset.GO and KEGG pathway analyses were carried out using Goatools and KOBAS27, respectively.
Proteomic data collection and analysis
Materials were frozen in liquid nitrogen and then transported to MajorBio (Shanghai, China) for proteomic data collection. Trypsin-digested peptides were analyzed by an EASY nLC-1200 system (Thermo, USA) coupled with a timsTOF Pro2 (Bruker, Germany) mass spectrometer. Briefly, the C18-reversed phase column (75 μm x 25 cm, Ionopticks, USA) as equilibrated with solvent A (A:2% ACN with 0.1% formic acid) and solvent B (B: 80% ACN with 0.1% formic acid).The peptides were eluted using the following gradient: 0–45 min, 3-28% B; 45–50 min, 28−44%B; 50–55 min, 44−90% B; 55–60 min,90%−90% B. The tryptic peptides were separated at a flow rate of 250 nL/min. Peptides were separated by an ultrahigh performance liquid phase system subjected to a capillary ion source and then analyzed by timsTOF Pro2 (Bruker, Germany) ; the electrospray voltage was 1.5 kV. The peptide parent ions and their secondary fragments were detected and analyzed using high-resolution TOF. The secondary MS scanning range was 100–1700 m/z. Data acquisition on the timsTOF Pro2 was collected using the parallel accumulation serial fragmentation (PASEF) acquisition mode. After the first MS stage, the second MS stage (charge number of the parent ions was 0–5) was recorded using the 10 PASEF mode. A dynamic exclusion time of 24 s was used for the MS/MS scan.
MS/MS spectra were searched using MaxQuant version 2.0.3.1 software against a self-built database using genomic data constructed by MajorBio. The highest score for a given peptide mass (best match to that predicted in the database) was used to identify parent proteins. The parameters for protein searching were set as follows: tryptic digestion with up to two missed cleavages, carbamidomethylation of cysteines as fixed modification, and oxidation of methionines and protein N-terminal acetylation as variable modifications. False discovery rate (FDR) of peptide identification was set as FDR ≤ 0.01. A minimum of one unique peptide identification was used to support protein identification.
Bioinformatic analysis of proteomic data was performed with the Majorbio Cloud platform (https://cloud.majorbio.com)28. P-values and Fold change (FC) for the proteins between the two groups were calculated using R package “t-test”. The thresholds of fold change (> 1.2 or http://geneontology.org/) and KEGG pathway (http://www.genome.jp/kegg/).Protein-protein interaction analysis was performed using the String v11.5.
Metabolomic data collection and analysis
The metabolomic data was obtained from two sets of treatments, T0 and T2, with each treatment providing 6 samples for data collection, that is, one treatment had 6 repeated data collection experiments. The materials have been submitted to the company (MajorBio, Shanghai, China) for processing and data analysis.
50 mg solid sample was added to a 2 mL centrifuge tube and a 6 mm diameter grinding bead was added. 400 µL of extraction solution (methanol: water = 4:1 (v: v)) containing 0.02 mg/mL of internal standard (L-2-chlorophenylalanine) was used for metabolite extraction. Samples were ground by the Wonbio-96c ( Shanghai wanbo biotechnology co., LTD) frozen tissue grinder for 6 min (-10 °C, 50 Hz), followed by low-temperature ultrasonic extraction for 30 min (5 °C, 40 kHz). The samples were left at -20 °C for 30 min, centrifuged for 15 min (4 °C, 13000 g), and the supernatant was transferred to the injection vial for LC-MS/MS analysis.
As a part of the system conditioning and quality control process, a pooled quality control sample (QC) was prepared by mixing equal volumes of all samples. The QC samples were disposed and tested in the same manner as the analytic samples. It helped to represent the whole sample set, which would be injected at regular intervals (every 5–15 samples) in order to monitor the stability of the analysis.
The LC-MS/MS analysis of sample was conducted on a Thermo UHPLC-Q Exactive HF-X system equipped with an ACQUITY HSS T3 column (100 mm × 2.1 mm i.d., 1.8 μm; Waters, USA) at Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The mobile phases consisted of 0.1% formic acid in water: acetonitrile (95:5, v/v) (solvent A) and 0.1% formic acid in acetonitrile: isopropanol: water (47.5:47.5, v/v) (solvent B). Positive ion mode separation gradient: 0–3 min, mobile phase B was increased from 0 to 20%; 3–4.5 min, mobile phase B was increased from 20 to 35%; 4.5–5 min, mobile phase B was increased from 35 to 100%; 5–6.3 min, mobile phase B was maintained at 100%; 6.3–6.4 min, mobile phase B was decreased from 100 to 0%; 6.4–8 min, mobile phase B was maintained at 0%. Separation gradient in negative ion mode: 0–1.5 min, mobile phase B rises from 0 to 5%; 1.5–2 min, mobile phase B rises from 5 to 10%; 2–4.5 min, mobile phase B rises from 10 to 30%; 4.5–5 min, mobile phase B rises from 30 to 100%; 5–6.3 min, mobile phase B linearly maintains 100%; 6.3–6.4 min, the mobile phase B decreased from 100 to 0%; 6.4–8 min, the mobile phase B was linearly maintained at 0%. The flow rate was 0.40 mL/min and the column temperature was 40℃.
The mass spectrometric data were collected using a Thermo UHPLC-Q Exactive HF-X Mass Spectrometer equipped with an electrospray ionization (ESI) source operating in positive mode and negative mode. The optimal conditions were set as followed: source temperature at 425℃ ; sheath gas flow rate at 50 arb; Aux gas flow rate at 13 arb; ion-spray voltage floating (ISVF) at -3500 V in negative mode and 3500 V in positive mode, respectively; Normalized collision energy, 20-40-60 V rolling for MS/MS. Full MS resolution was 60,000, and MS/MS resolution was 7500. Data acquisition was performed with the Data Dependent Acquisition (DDA) mode. The detection was carried out over a mass range of 70–1050 m/z.
The pretreatment of LC/MS raw data was performed by Progenesis QI (Waters Corporation, Milford, USA) software. The metabolites were identified by searching database, and the main databases were the HMDB (http://www.hmdb.ca/), Metlin ( https://metlin.scripps.edu/) and Majorbio Database .
The data matrix obtained by searching database was uploaded to the Majorbio cloud platform (https://cloud.majorbio.com)28 for data analysis. The R package “ropls”(Version 1.6.2)29 was used to perform principal component analysis (PCA) and orthogonal least partial squares discriminant analysis (OPLS-DA), and 7-cycle interactive validation evaluating the stability of the model. The metabolites with VIP > 1, p
Differential metabolites among two groups were mapped into their biochemical pathways through metabolic enrichment and pathway analysis based on KEGG database (http://www.genome.jp/kegg/). Python packages “scipy.stats” (https://docs.scipy.org/doc/scipy/ )30 was used to perform enrichment analysis to obtain the most relevant biological pathways for experimental treatments.
Multi-omics data analysis and statistical analysis
Data analysis within each omics discipline was primarily conducted on the Majorbio cloud platform (https://cloud.majorbio.com)28. Comprehensive multi-omics data analysis was performed in the R environment version 4.3.1. Data cleaning and visualization were carried out using the tidyverse package31 and pathview package32,33,34,35, while the construction and analysis of regulatory networks utilized the igraph package36. GSEA analysis was conducted offline employing the GSEA software37,38, and the results were enhanced using the R package GseaVis39. Color palettes for visualization were implemented using the ggsci package40. The data of phenotype and active component content were analyzed by one-way ANOVA and LSD (α = 0.05) for multiple comparison tests.