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Insights into infraspecific differentiation of the medicinally important species Bupleurum Chinense revealed by morphological and molecular evidence

BMC Plant Biology volume 25, Article number: 626 (2025) Cite this article

Abstract

Background

Radix Bupleuri, derived from the dried roots of Bupleurum chinense DC., is a well-documented phytomedicine in global pharmacopoeias and a common constituent in herbal formulations. While previous studies have hinted at regional variations in the chemical composition of B. chinense, a comprehensive understanding of its morphological, genetic, and chemical diversity across China remains incomplete.

Objective

This study aims to investigate the infraspecific variation of B. chinense by analyzing its morphological, genetic, and chemical phenotypes.

Methods

Wild B. chinense specimens were collected from 31 locations spanning nine Chinese provinces/municipalities, representing a wide range of its natural distribution. A multi-faceted approach combining 21 morphological traits, plastid genome sequencing, and chemical analysis was employed to explore infraspecific variation and clustering patterns.

Results

Distinct infraspecific variation was revealed through integrated morphological and molecular data. Morphological clustering analysis identified two geographically associated clusters, roughly corresponding to coastal and inland regions. Although plastid genome sequencing of 40 specimens showed high sequence identity, population structure analysis detected variable hotspots. Both maximum likelihood (ML) tree and population structure results consistently identified three distinct clades, which mirrored the patterns observed in morphological clustering. Quantitative analysis of saikosaponins content in 10 representative specimens across the three clades demonstrated significant chemotype variation. Notably, samples from Anhui Province exhibited the highest saikosaponins content, while those from Shanxi Province showed the lowest levels. This chemotype variation, coupled with observed genetic diversity, suggests that B. chinense germplasm from Clade I (particularly from Anhui Province) represents a promising wild resource for further development.

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Introduction

Radix Bupleuri is a well-documented traditional herbal medicine with a history of use spanning thousands of years. First mentioned in Shennong’s Herbal Classic, it has been highly regarded in Traditional Chinese Medicine for its ability to regulate liver function, promote Qi flow, enhance blood circulation, and relieve pain [1]. Its widespread application is evidenced by its inclusion in the pharmacopoeias of China, Europe, Japan, and Korea [2]. Furthermore, Radix Bupleuri serves as a key component in 549 traditional Chinese patent medications, including popular formulations such as Xiaoyao Pills, Xiao Chaihu Granules, and Ganmao Qingre Granules [2, 3]. The demand for Radix Bupleuri has experienced steady growth, reaching over 9,000 tons in 2022 [4, 5].

Bupleurum chinense DC., the primary source plant for Radix Bupleuri in China, is a taxonomically complex species within the Chinese Bupleurum flora [6,7,8]. This plant is primarily distributed in China, especially in northeastern, northern, northwestern, and central China. Despite its significance in traditional medicine, the infraspecific variation of B. chinense remains poorly understood [7,8,9]. Infraspecific variation can lead to distinct chemical profiles, affecting the quality of the medicinal product [10,11,12,13].

Bupleurum chinense is a perennial, cross-pollinated herb that is widely distributed throughout its Chinese range. This extensive range contributes to genetic differentiation and diverse morphological characteristics, suggesting significant germplasm diversity [8,9,10, 14, 15]. This diversity may impact the quality of the medicinal material. Studies have shown significant variation in saikosaponin content, the key bioactive components of Radix Bupleuri, with levels differing up to tenfold among plants from different origins [10,11,12]. Additionally, the hepatoprotective effects of Radix Bupleuri vary by region [13].

However, previous reports on the morphology and genetic variation of B. chinense are limited due to small sample sizes and a narrow geographical distribution. Furthermore, there is insufficient genetic information based on DNA barcodes such as ITS, matK, rbcL, and psbA-trnH [8, 9, 15]. These limitations make it challenging to clarify the infraspecific lineage division and phylogenetic relationships of B. chinense.

Given the limited understanding of infraspecific variation, optimal development, utilization, and clinical application of Radix Bupleuri are hindered. Therefore, a comprehensive investigation into the infraspecific variation of B. chinense is essential to enhance its safety and effectiveness in traditional medicine.

In the present study, aimed at gaining a more comprehensive understanding of infraspecific variation in B. chinense, we adopted an integrated approach to analyze 21 key morphological traits and the plastid genome of wild B. chinense specimens collected from a broader geographic range. Additionally, representative samples were selected for saikosaponin content analysis.

Plant materials and chemical reagents

Wild B. chinense specimens were collected from 31 natural populations across nine Chinese provinces/municipalities (detailed in Table 1), encompassing the species’ core distribution range as described in Flora of China [8]. All voucher specimens were botanically authenticated by the corresponding author and deposited in the Herbarium of Southern Medical University. Leaf samples were preserved using silica gel desiccation for subsequent plastid genome analysis.

Table 1 Sample information of B. chinense in China
Full size table

Reference standards of saikosaponins [saikosaponin a: (SSa, > 98%), saikosaponin c: (SSc, > 97%), saikosaponin d (SSd, > 98%)] were purchased from Shanghai Yuanye Biotechnology Co., Ltd. HPLC-grade reagents and solvents were obtained from Fuzhou Aoyan Experimental Equipment Co., Ltd., while other analytical-grade chemicals were provided by Guangzhou Chemical Reagent Factory.

Morphological characterization

A total of 126 wild-collected B. chinense specimens from 25 geographic populations were analyzed, excluding those lacking critical morphological features (e.g., rays, florets, bracts, and bracteoles) (detailed in Table 1). To enhance geographic representation, morphological data from 42 additional herbarium specimens (Chinese Virtual Herbarium: http://www.cvh.ac.cn) were included, covering Northeast China (e.g. Liaoning, Jilin) and coastal regions (e.g. Jiangsu, Zhejiang) (Table S1).

Twenty-five morphological traits were quantified across 168 specimens, including: (1) Vegetative traits: Plant height, root length, root diameter, root branching, and stem branching; (2) Foliar traits: Length and width of basal, middle, and upper leaves; (3) Reproductive traits: Umbel diameter, ray number, floret number, bract/bracteole number & size (length/width).

Due to incomplete data availability in herbarium specimens (notably root parameters and bract numbers), 21 standardized traits were retained for analysis. Morphological classification was based on length-to-width ratios (LWR):

  • Leaves: Ovate-lanceolate (LWR < 6), elliptic-lanceolate (6–12), linear-lanceolate (> 12).

  • Bracts: Long elliptic-lanceolate (LWR ≤ 4), narrow-lanceolate (4–8), lanceolate (> 8).

  • Bracteoles: Ovate-lanceolate (LWR < 4), elliptic-lanceolate (4–8), narrow-lanceolate (> 8).

Morphological data were standardized (Z-score normalization) and analyzed via Principal Component Analysis (PCA) using SPSS 21.0. Hierarchical clustering was performed using between-groups linkage with average Euclidean distances. Continuous variables are expressed as mean ± SD. Statistical significance (P < 0.05) was assessed using One-way ANOVA for normally distributed, homogeneous variance data, and Mann-Whitney U test for non-parametric data.

DNA extraction, sequencing, plastid genome assembly, and annotation

Total genomic DNA was isolated from specimens using the modified CTAB protocol [16]. The DNA integrity and purity were assessed through 1.2% agarose gel electrophoresis and quantification with a NanoDrop 2000 C spectrophotometer (Thermo Fisher Scientific Inc. MA, USA). High-throughput sequencing was performed on the BGI platform (Shenzhen, China) to generate 150-bp paired-end raw reads, yielding approximately 3 GB of data per sample. Raw sequencing data underwent quality control using SOAPnukev2.1.5 [17] to eliminate adapter sequences and low-quality reads.

Plastid genomes were de novo assembled using GetOrganelle v1.7.3 [18] with the reference genome NC_046774 [19] from GenBank. The resulting assemblies were verified through sequence alignment in Geneious Prime [19]. Dual annotation strategies were implemented using GeSeq [20] (https://chlorobox.mpimp-golm.mpg.de/geseq.htm) and PGA [21], with subsequent manual curation of start/stop codons in Geneious. tRNA genes were validated through cross-referencing between tRNAscan-SE v2.0.7 [22] and ARAGORN v1.2.38 [23] predictions. Final genome visualization was generated using OGDRAW (http://ogdraw.mpimp-golm.mpg.de/).

Comparative plastid genome analysis

A comprehensive analysis was completed for 40 specimens from 27 collection sites, following the exclusion of samples with insufficient DNA purity (OD260/280 < 1.8) (Table 1). For comparative assessment of sequence polymorphisms, multiple sequence alignment was performed using MAFFT (https://mafft.cbrc.jp/alignment/server/), followed by computational quantification of genetic variability metrics in DnaSP v6 [24]. Specifically, two key parameters were analyzed: (1) total variable sites and (2) nucleotide diversity (Pi) within protein-coding sequences (CDS) and intergenic spacer regions.

Phylogenetic reconstruction and population structure inference

To resolve infraspecific relationships in B. chinense, a plastid genome-based maximum likelihood (ML) phylogeny was reconstructed with Bupleurum sikangense X. J. He & C. B. Wang designated as the outgroup [25]. The phylogenetic framework was established in IQ-TREE [26] implementing 1000 bootstrap replicates for node support assessment. Optimal substitution model selection (AIC criterion) was conducted through ModelFinder [27].

Demographic history reconstruction utilized STRUCTURE v2.3.4 [28] with parameter optimization, i.e. Burn-in period: 200,000 generations, MCMC replicates: 1,200,000 iterations, Genetic clusters (K): 1–9, Independent runs per K: 5.

Posterior probability analysis determined the optimal K-value via Structure Harvester, with population stratification visualized through distruct v1.1 [29] and CLUMPP v1.1.2 [30]. Genetic divergence metrics (P-distance) were computed in MEGA11 [31] to facilitate phytochemical correlation studies with saikosaponin profiles.

Genetic diversity, structure, and differentiation analyses

Nucleotide diversity (π) and haplotype diversity (Hd) were computed using DnaSP v6. Population genetic structure was assessed through Analysis of Molecular Variance (AMOVA) implemented in Arlequin v3.0, utilizing complete plastid genome sequences to quantify genetic variation distribution within and among B. chinense populations. The fixation index (FST) derived from AMOVA was subsequently transformed using the formula FST/ (1- FST) to generate a genetic distance matrix [32]. Spatial genetic patterns were investigated through Mantel tests performed in GenAlEx v6.5 [33], which examined the correlation between pairwise genetic distances and geographical distances across populations.

Quantification of saikosaponins

Based on phylogenetic tree analysis, representative samples were selected through stratified proportional sampling for determining SSa, SSc, and SSd contents. Test solutions were prepared according to the Chinese Pharmacopoeia [1], followed by filtration through 0.22 μm microporous membranes. Saikosaponin standards (SSa, SSc, SSd) were dissolved in methanol, with 10 µL aliquots injected into the HPLC system.

Chromatographic separation was performed using an Agilent 1260 Infinity II LC system (Agilent Technologies, Inc. CA, USA) equipped with a Supersil ODS2 column (250 × 4.6 mm, 5 μm; Dalian Elite Analytical Instruments Co., Ltd., China). The mobile phase consisted of acetonitrile (A) and water (B) at 1 mL/min flow rate, with UV detection at 200 nm. Column temperature was maintained at 25 °C. The gradient program was: 0–5 min (30% A), 5–10 min (50% A), 10–25 min (60% A), 25–30 min (90% A), 30–35 min (30% A).

Method validation included assessments of linearity, precision, repeatability, stability, and accuracy for all three saikosaponins. Content data from 10 B. chinense batches were analyzed using SPSS 21.0 to establish a distance matrix, followed by Pearson correlation analysis between saikosaponin content patterns and phylogenetic distances across different origins.

Results

Infraspecific morphological variation in B. chinense

The 25 measured morphological traits exhibited considerable variation across collected samples, with coefficients of variation (CV) ranging from 0 to 155.91% (mean 71.71 ± 44.14%; Table S2). Notably, bracteole number remained invariable (consistently five), while bract length (CV = 155.91%) and stem branch number (CV = 146.46%) demonstrated the highest variability. All morphological parameters except bracteole number showed significant differences among sample batches (P < 0.001).

Principal component analysis revealed six principal components (eigenvalues > 1) collectively explaining 74.0% of total variance (Table S3). Thirteen traits with absolute loading values (> 0.6) were identified as primary contributors to phenotypic diversity (Table S4). Hierarchical clustering based on these components segregated specimens into two distinct groups (Fig. S1). Cluster 1 specimens displayed greater morphological complexity, featuring more ray florets (7 vs. 6), narrower umbel diameter (3.44 vs. 4.85 mm), and reduced bract width (0.08 vs. 1.94 mm) compared to Cluster 2 (Fig. 1). Notably, Cluster 2 maintained uniform elliptic-lanceolate leaf morphology across both basal and median positions, whereas Cluster 1 exhibited heterophylly with linear-lanceolate basal leaves transitioning to elliptic-lanceolate median leaves.

Geographic analysis revealed distinct distribution patterns: Cluster 2 specimens predominantly originated from western China (Ningxia, Inner Mongolia, Gansu), while Cluster 1 comprised central (Henan, Hubei) and eastern populations (Jilin, Liaoning, Tianjin, Jiangsu, Anhui, Zhejiang, Fujian). Transitional regions along the west-east boundary (Beijing, Hebei, Shanxi, Shaanxi, Shandong) contained mixed populations from both clusters.

Fig. 1
figure 1

Geographical distribution of the morphological clustering analysis

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Structural organization of the B. chinense plastome

The complete plastid genomes of B. chinense demonstrated limited size variation (154,789 − 155,963 bp; Table S5), maintaining a conserved quadripartite architecture comprising two inverted repeats (IRs, 26,289 − 26,333 bp) flanking large and small single-copy regions (LSC: 84,682 − 86,715 bp; SSC: 17,487 − 17,602 bp; Fig. 2a). Notably, the LSC exhibited the greatest sequence length variability (CV = 0.0031%), contrasting with the highly stable IR regions (CV = 0.0004%) (Table S5). Three accessions (BJ2, HBQX_1, HBLSa) showed a marginally reduced GC content of 37.6%, while all others maintained a conserved 37.7%.

Fig. 2
figure 2

Plastid genome structure of B. chinense and comparative analysis of plastid genome within B. chinense. (a) Plastid genome map of B. chinense. (b) Nucleotide diversity (Pi) of genes and spacer regions

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The plastomes encoded 133–134 functional genes, including 86–87 protein-coding genes (PCGs), 37 tRNAs, and 8 rRNAs (Table S5). Duplicated elements within IR regions comprised 7 PCGs (rps7, rps12, ndhB, rpl2, ycf2, ycf15), 7 tRNAs (trnA-UGC, trnI-CAU, trnI-GAU, trnL-CAA, trnN-GUU, trnR-ACG, trnV-GAC), and 4 rRNAs. Intronic sequences were identified in 18 genes, with 15 containing single introns and three possessing dual introns (Table S6). Pseudogenization was observed in two boundary-spanning genes: rps19 (SSC/IRb junction) and ycf1 (SSC/IRa junction). A distinctive T-base deletion in the rpoA gene of SXWTb caused pseudogenization, resulting in this accession’s reduced gene complement relative to others.

Plastid genome variation and genetic diversity in B. chinense

Analysis using MEGA 11 revealed a plastid genome dataset spanning 159,278 bp, containing 2,343 variable sites (1.47%) and 155,798 conserved sites (97.81%), with the remainder comprising indels. Among the variable sites, 688 were single-base mutations and 1,650 were parsimony-informative sites. Nucleotide diversity (Pi) analysis identified hypervariable regions, with values ranging from 0 to 0.03983 (mean = 0.00342). The atpF-atpH spacer exhibited the highest Pi value (0.03983), consistent with spacer regions showing greater genetic diversity than coding regions (Fig. 2b).

We identified nine highly variable gene regions (Pi > 0.0030): matK, ndhF, ndhH, ndhK, psaJ, rpl33, rps15, rps16, and ycf1. Ten spacer regions displayed even higher variability (Pi > 0.010): psbK-psbI, trnS-GCU-trnG-UCC, atpF-atpH, atpI-rps2, petN-psbM, petA-psbJ, rps32-trnL-UAG, ndhE-ndhG, ndhG-ndhI, and rps15-ycf1.

Analysis of 40 B. chinense plastid genomes revealed 34 distinct haplotypes, each unique to individual accessions. Population-level analyses showed haplotype diversity (Hd) ranging from 0 (Shandong, Inner Mongolia, Ningxia) to 0.985 (Hebei), while nucleotide diversity (π) varied from 0 (Shandong, Inner Mongolia, Ningxia, Gansu) to 0.00675 (Beijing) (Table S7).

Phylogenetic relationships and population structure

The ML phylogenetic analysis using the GTR + F + I + G4 substitution model revealed three strongly supported clades (bootstrap values > 70% for most nodes; Fig. 3a). Clade I comprised 20 accessions from Shanxi, Hebei, Anhui, Beijing, Inner Mongolia, and Shandong. Clade III included 16 accessions from Shanxi, Gansu, Shaanxi, Ningxia, and Hebei. The remaining four accessions (3 from Hebei, 1 from Beijing) formed Clade II (Fig. 3b). Genetic distances among accessions ranged from 0 to 0.0053 (mean = 0.0026), with 780 pairwise comparisons analyzed (Fig. S2).

Fig. 3
figure 3

Phylogenetic analysis. (a) ML tree of B. chinense based on whole plastid genomes. The numbers above the branches are bootstrap values in order. (b) Geographic distribution of the three clades in B. chinense (the sample used for detecting the content of saikosaponin is marked with a yellow square). (c) SSa + SSd content of 10 different batches of B. chinense. (d) SSa content of 10 different batches of B. chinense. (e) SSc content of 10 different batches of B. chinense. (f) SSd content of 10 different batches of B. chinense

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Population structure analysis identified K = 3 as the optimal cluster configuration. The 40 individuals of B. chinense segregated into three genetic groups, mirroring the ML tree topology (Fig. 3). At K = 2, populations diverged into northwestern (Shaanxi, Ningxia, and Gansu) and eastern (Anhui, Shandong, and Inner Mongolia) clusters, while central populations (Shanxi, Hebei, and Beijing) showed mixed ancestry. At K = 3, central populations emerged as a distinct cluster, establishing a refined northwest-north-center population structure that aligns with geographical distributions (Fig. 4).

Fig. 4
figure 4

STRUCTURE analysis (The different colors represent the different genetic clusters. Red, light green and blue represent the group I, II, III, respectively.)

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Genetic diversity, structure, and differentiation in B. chinense subgroups

Based on STRUCTURE analysis, the 40 plastid genomes were stratified into three genetically distinct groups. No haplotypes were shared between groups, with Hd ranging from 0.726 (Group II) to 0.833 (Group III) and nucleotide diversity (π) varying between 0.00165 (Group II) and 0.00231 (Group I) (Table S8). AMOVA of complete plastid genomes revealed significant genetic partitioning, with 63.64% of total variation occurring among groups and 36.36% within groups (P < 0.001; Table S9).

Pairwise comparisons showed high genetic differentiation (mean FST= 0.6364), with the strongest divergence between Group II and Group III (FST= 0.7517), followed by Group I vs. Group III (FST= 0.6945) and Group I vs. Group II (FST= 0.5746) (Table S10). Gene flow estimates indicated limited population connectivity (mean Nm = 0.1428). Mantel tests demonstrated significant isolation-by-distance patterns, with geographical separation explaining much of the observed genetic differentiation (P = 0.014; Fig. S3).

Phytochemical variation analysis

The established analytical method demonstrated excellent linearity for SSa, SSc, and SSd within their respective concentration ranges (R²>0.999; Table S11, Fig. S4). Method validation results confirmed satisfactory precision, repeatability, stability, and accuracy (Tables S12-S13). Through stratified proportional sampling (4:1 ratio), we analyzed 10 B. chinense batches representing three phylogenetic clades: Clade I (AHJZ, AHLA, AHFX, SXWTb, HBXTb), Clade II (HBLSa), and Clade III (HBSXa, HBXTa, SXTY, SXWTa).

Our findings revealed significant geographical variations in phytochemical composition across the 10 B. chinense populations (P < 0.001). The quantified components demonstrated distinct concentration ranges, i.e. SSa: 0.197% (SXTY) to 1.206% (HBXTb), SSc: 0.134% (HBSX) to 0.534% (HBXTb), SSd: 0.157% (SXTY) to 1.343% (AHFX) (Fig. 3; Table S14).

All samples met the Chinese Pharmacopoeia standard for combined SSa + SSd content (> 0.30%), with AHLA showing the highest total (2.402%), followed by AHFX (2.243%) and HBXTb (2.183%). Spatial distribution patterns of saikosaponin levels are detailed in distance matrices (Tables S15-S17). Notably, Pearson analysis indicated no significant association between phytochemical profiles and genetic divergence (Table S18).

Discussion

Due to habitat heterogeneity or genetic divergence, species frequently exhibit extensive infraspecific variations across morphological, physiological, and genetic dimensions [33,34,35,36]. In medicinal plants, such infraspecific variability often manifests as distinct chemical phenotypes, potentially resulting in inconsistent medicinal quality [10,11,12,13]. Therefore, investigating infraspecies variation in medicinal plants establishes a crucial foundation for evaluating germplasm resources while offering theoretical insights for their sustainable utilization.

Morphological differentiation in B. chinense

For a long time, B. chinense has been considered a polymorphic species. The Flora of China documents three formae: f. octoradiatum (Bunge) R.H.Shan & M.L.Sheh, f. pekinense (Franch. ex Hemsl.) R.H.Shan & Y.Li, and f. chiliosciadium (H.Wolff) R.H.Shan & Y.Li [8]. Among them, f. chiliosciadium had been reduced to a synonym of B. scorzonerifolium Willd [6]. The two accepted formae exhibit considerable morphological overlap with the original forma in traits such as the number of umbel rays, leaf texture, and color, making them frequently indistinguishable in practical work. Consequently, very few specimens collected in the current study can be accurately identified at the forma level. Moreover, some researchers argue that formae hold limited significance in taxonomy studies [37]. After thorough consideration, we have opted not to employ the forma taxonomic rank in our study.

Our findings reveal significant morphological divergence within B. chinense populations, likely shaped by synergistic interactions between environmental pressures, natural selection, and genetic predisposition [34, 35, 38]. These factors collectively explain the pronounced interpopulation variations. Notably, we identified two distinct morphological types corresponding to geographic distribution: central-eastern versus central-western Chinese ecotypes. Diagnostic traits including basal leaf morphology, ray number, and bracteole configuration - previously established as key identifiers for B. chinense and its variants [6, 8] - demonstrated systematic regional variation across five measured characteristics (basal/mid-leaf morphology, ray number/umbel diameter, and bract width). This geographical patterning provides critical insights for the construction of a germplasm classification system and in-wild identification of B. chinense, while offering valuable perspectives for the origin and evolution of B. chinense.

The observed east-west morphological gradient may reflect underlying phylogenetic divergence within B. chinense, a proposition awaiting molecular validation. This spatial differentiation pattern underscores the need for further investigation into the species’ genetic architecture and adaptive mechanisms.

Potential markers for infraspecific classification and species identification in B. chinense

The infraspecific variations of B. chinense were analyzed through comparative plastid genome studies. All 40 examined plastid genomes exhibited high conservation in genome structure, gene content, and gene arrangement. Notably, nine coding regions and ten intergenic spacer regions demonstrated elevated nucleotide diversity (Pi values), revealing significant potential as phylogenetic markers for infraspecific classification and species identification in B. chinense.

For practical application in molecular authentication, ideal markers should meet dual criteria: (1) sufficient polymorphism for discrimination, and (2) reliable amplification feasibility in processed herbal materials, with optimal fragment sizes between 700 and 1500 bp [39]. Among these candidate regions, ndhH, rps16, and the intergenic spacers trnS-GCU-trnG-UCC, petN-psbM, petA-psbJ, and rps32-trnL-UAG were identified as the most suitable markers for population genetics studies in this species.

Infraspecific genetic differentiation of B. chinense

Emerging evidence from multilocus DNA analyses indicates significant infraspecific variations within B. chinense, supporting its classification as a taxonomically complex group undergoing active diversification [9, 10, 40]. Previous investigations, constrained by limited genetic markers or restricted sampling, provided inconclusive evidence for population differentiation. In contrast, our study integrates comprehensive sampling with multi-gene sequence datasets, enabling robust phylogenetic reconstruction and population structure analysis.

Phylogenetic resolution revealed three distinct monophyletic clades within B. chinense, a topological pattern corroborated by STRUCTURE analysis identifying three corresponding genetic clusters. These congruent results confirm pronounced infraspecific differentiation, with three genetically divergent lineages emerging. Spatial autocorrelation analysis via Mantel test demonstrated significant isolation-by-distance patterns (r = 0.423, P < 0.01), suggesting geographical isolation as a key driver of population divergence. Such phylogeographic structuring aligns with documented patterns in Ormosia henryi Prain [41], Corybas taliensis Tang & F.T. Wang [42], and Avicennia marina (Forssk.) Vierh [43]. Intriguingly, northern Chinese populations exhibited mosaic genetic profiles, potentially reflecting historical admixture events between divergent ancestral lineages, and their secondary mixture made it having high genetic diversity.

All three lineages displayed high haplotype diversity (Hd > 0.85) coupled with low nucleotide diversity (π < 0.005), accompanied by complete haplotype exclusivity—signatures indicative of prolonged genetic isolation and limited interpopulation gene flow [44]. Hierarchical AMOVA attributed 78.3% of total genetic variation to intergroup differences, underscoring lineage divergence as the dominant evolutionary force. Quantified differentiation indices (FST= 0.31–0.42) exceeded Wright’s threshold for “very high” genetic differentiation (FST> 0.25) [45], a phenomenon potentially exacerbated by restricted pollen/seed dispersal mechanisms. This pattern parallels findings from psbA-trnH marker analyses in related taxa [46].

Accumulating evidence suggests genetic factors significantly influence the biosynthesis of plant secondary metabolites [47]. Contrary to this paradigm, our study revealed no significant correlation (P > 0.05) between saikosaponin content (SSa, SSc, SSd) in B. chinense and genetic divergence metrics, demonstrating that interpopulation variations in these bioactive compounds are likely governed by non-genetic determinants. Specifically, edaphic parameters (e.g., soil pH, nutrient availability) and environmental conditions (e.g., precipitation regimes, temperature fluctuations) may serve as primary drivers of saikosaponin heterogeneity across sampled populations, as evidenced by complementary studies linking phytochemical diversity to ecological variables [47,48,49].

Comparative analysis of morphological and genetic differentiation

While morphological and genetic differentiation patterns broadly align in their east-west divergence trends, notable inconsistencies emerge between the datasets. Specifically, specimens from Inner Mongolia, Gansu, and Ningxia formed a cohesive morphological cluster but were partitioned into distinct genetic branches. Conversely, central populations (Shanxi, Hebei, Beijing) separated into two morphological clusters yet exhibited tripartite genetic differentiation, revealing finer-scale population structuring at the molecular level.

Such discrepancies between infraspecific morphological and molecular variation are well-documented across plant taxa, including Ducrosia anethifolia (DC.) Boiss [50]., Glycyrrhiza glabra L [34]. and Brassica juncea (L.) Czern [51]. Three primary factors may explain these discordances: (1) Morphological traits primarily manifest coding region polymorphisms, while molecular markers integrate both coding and non-coding genetic information [50, 52]; (2) Historical gene flow and incomplete lineage sorting may decouple phenotypic-genetic associations [35], particularly given Bupleurum’s cross-pollination system facilitating intergroup genetic exchange [50]; (3) Habitat fragmentation and local adaptation processes may differentially influence morphological development.

Methodological considerations include potential spatial sampling biases and unequal sample sizes between morphological and genetic analyses. Notably, while plastome sequencing effectively reveals maternal lineage patterns, its non-recombining nature and uniparental inheritance [53] limit detection of nuclear gene flow and introgression events. Such limitations commonly produce discordance between plastid and nuclear phylogenies [54]. Future investigations incorporating nuclear ribosomal DNA or single-copy nuclear genes will be crucial to validate these findings and comprehensively characterize B. chinense’s infraspecific variation.

Conclusions

This multidisciplinary investigation explored the infraspecific variation of B. chinense through comprehensive genetic, morphological, and phytochemical analyses. Substantial morphological diversity was observed within B. chinense, despite the highly conserved plastid genome. Both morphological and genomic evidence confirmed that differentiation has occurred within the species. The majority of the B. chinense resources examined in this study exhibited significant genetic diversity, particularly among samples from Shanxi, Hebei, and Anhui provinces. Phytochemical analysis further revealed that B. chinense accessions collected from Anhui province possessed both high genetic diversity and elevated saikosaponin content, making them an exceptional source for high-quality Radix Bupleuri. These findings provide valuable guidance for the rational utilization of B. chinense resources, ensuring the efficacy and safety of its clinical application.

Data availability

Sequence information of 40 complete plastid genomes is available in the NCBI database (https://www.ncbi.nlm.nih.gov/genbank/)(GenBank accessions were showed in Table 1).

Abbreviations

ITS:

Internal transcribed spacer

CD:

Coding gene

ML:

Maximum likelihood

SSa:

Saikosaponin a

SSc:

Saikosaponin c

SSd:

Saikosaponin d

IR:

Inverted repeat

LSC:

Large single-copy

SSC:

Small single-copy

CV:

Variable coefficient

tRNA:

transfer RNA

rRNA:

ribosome RNA

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Acknowledgements

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Funding

This work was supported by the National Natural Science Foundation of China [grant number 30873387, 81373905, 82474025]; the Natural Science Foundation of Guangdong Province [grant number 2014A030313321]; and China Postdoctoral Science Foundation [grant number 2021M691482].

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  1. Fang Li and Kaiying Ge contributed equally to this work.

Authors and Affiliations

  1. Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China

    Fang Li & Zhi Chao

  2. Faculty of Medicinal Plants and Pharmacognosy, School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510515, China

    Kaiying Ge, Rong Huang, Yi Lu, Huihui Lin, Yanfengyang Jiang, Enwei Tian & Zhi Chao

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Contributions

F.L.: Conceptualization, Methodology, Software, Formal analysis, Writing - original draft. K.G.: Methodology, Data curation. R.H.: Conceptualization, Methodology, Writing- Original draft preparation, Funding acquisition. Y.L.: Data curation. H.L.: Data curation. Y.J.: Data curation. E.T.: Methodology. Z.C.: Supervision, Project administration, Conceptualization, Writing- Reviewing and Editing, Funding acquisition.

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Correspondence to Zhi Chao.

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Li, F., Ge, K., Huang, R. et al. Insights into infraspecific differentiation of the medicinally important species Bupleurum Chinense revealed by morphological and molecular evidence. BMC Plant Biol 25, 626 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12870-025-06661-0

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BMC Plant Biology

ISSN: 1471-2229

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