Combined drought and high temperature stress enhances saikosaponin biosynthesis in Bupleurum Chinense through multi-level regulatory mechanisms

Linlin Yang; Xupeng Gu; Shengwei Zhou; Jie Wan; Lu Qiao; Ning Dong; Leixia Chu; Chengming Dong; Weisheng Feng

doi:10.1186/s12870-025-07866-z

. 2025 Dec 22;26:141. doi: 10.1186/s12870-025-07866-z

Combined drought and high temperature stress enhances saikosaponin biosynthesis in Bupleurum Chinense through multi-level regulatory mechanisms

Linlin Yang ^1,^3,^4,^✉,^#, Xupeng Gu ^1,^#, Shengwei Zhou ¹, Jie Wan ², Lu Qiao ^1,^3,⁴, Ning Dong ^1,^3,⁴, Leixia Chu ^1,^3,⁴, Chengming Dong ^1,^3,⁴, Weisheng Feng ^1,^3,⁴

PMCID: PMC12836868 PMID: 41430104

Abstract

Background

As an important medicinal plant, the synthesis of active ingredient saikosaponin in Bupleurum chinense DC. is significantly influenced by environmental factor stress. However, there is limited understanding of how secondary metabolites in medicinal plants change under the multiple combined stresses.

Results

This study investigated the changes in saikosaponins content in Bupleurum chinense under drought combined with temperature stress, as well as the potential molecular and ecological mechanisms involved. The experimental plants were divided into four groups: control (CK), drought with low temperature (DL), drought with medium temperature (DM), and drought with high temperature (DH). DL, DM, and DH inhibited plant height and root length, with DH exhibiting the strongest inhibitory effect. The degree of stress followed the order DH > DM > DL > CK. B. chinense adapted to the combined stresses by modulating osmotic regulatory substances and protective enzyme activities. Under combined stress, five enzymes in the terpenoid synthesis pathway showed positive changes. The content of saikosaponins, specifically saikosaponin a and saikosaponin d, increased significantly under DH treatment in the short term. On day 4 of treatment, their levels reached 4.52, 2.07, and 1.54 mg/g, representing increases of 40.82%, 32.76%, and 36.97%, respectively, compared to CK. Abscisic acid levels under DL, DM, and DH were 12.90-, 6.38-, and 16.27-fold higher, respectively, than those under CK. High-quality transcriptome sequencing revealed active changes in gene expression profiles. Weighted gene co-expression network analysis identified the turquoise module genes (12,753 target genes), which were strongly correlated with physiological indices, plant hormones, functional enzymes, and saikosaponins. Saikosaponins synthesis was found to be regulated by multiple transcription factors and functional genes, with distinct regulatory networks governing abscisic acid and saikosaponins.

Conclusions

Our findings demonstrate that the synthesis and accumulation of saikosaponins exhibit a “short-term promotional effect” under combined stress, regulated by multiple factors. This study elucidates the molecular and ecological mechanisms underlying the rapid accumulation of saikosaponins and provides technical insights for ecological strategies to support B. chinense high-quality cultivation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-025-07866-z.

Keywords: Bupleurum chinense, Combined stress, Molecular ecological mechanism, Saikosaponins biosynthesis

Introduction

Bupleurum chinense DC. is a perennial herbaceous plant belonging to the family Apiaceae and the genus Bupleurum [1]. According to the Chinese Pharmacopoeia, the dried roots of B. chinense and Bupleurum scorzonerifolium Willd. are designated as the medicinal herb (Chaihu in Chinese) [2]. However, B. chinense currently holds a greater market share than B. scorzonerifolium in the circulating Chinese medicinal herbs market [3]. Recent estimates indicate that the annual domestic demand for B. chinense in China ranges between 5,500 and 6,000 tons, with annual production exceeding 5,000 tons in typical years. In addition to high domestic demand, B. chinense is also in significant demand in international markets, with exports reaching over 30 countries and regions. There are nearly 1,900 prescriptions incorporating B. chinense, including the Xiaochaihu decoction, Dachaihu decoction, and Chaihu Guizhi decoction [4]. The therapeutic effects of B. chinense are attributed to saikosaponins, a class of pentacyclic triterpenoid oleanane derivatives [5]. The synthesis and accumulation of saikosaponins result from the ecological responses of medicinal plants to external environmental changes [6]. However, the molecular mechanisms through which environmental factors influence saikosaponins synthesis remain poorly understood.

Huang Luqi proposed the “stress effect hypothesis” regarding the quality formation of genuine medicinal materials [7], pointing out that genuine medicinal material properties are shaped by accumulated environmental stresses. The phenotype of medicinal plants, manifested in their content of bioactive components, is collectively shaped by both genetic factors (genotype) and environmental conditions [8]. Changes in ecological factors regulate the synthesis and accumulation of secondary metabolites in plants [9]. The essence of genuine medicinal materials lies in the biosynthesis, accumulation, and transformation of their medicinal components under ecological influences. This concept is summarised as “genotype determines medicinal properties, and ecology manifests medicinal properties” [10]. Elucidating the “stress effect hypothesis” for the quality formation of B. chinense and understanding the molecular and ecological regulatory mechanisms of saikosaponin synthesis and accumulation have significant theoretical and practical implications. This understanding is crucial for ensuring the stable and controllable quality of medicinal materials and advancing ecological planting technologies for Chinese medicinal plants.

B. chinense is primarily distributed in arid and semi-arid regions characterised by extensive geographical variation and significant temperature differences between day and night [11]. Water and temperature are key factors influencing global ecological heterogeneity patterns, including vegetation changes and biodiversity [12]. For individual plants, water acts as the carrier for material transport, while temperature supports enzymatic activities essential for life processes [13]. Our research group has conducted molecular, ecological, and physiological studies on saikosaponin synthesis and accumulation in B. chinense under drought stress, revealing regulatory mechanisms in response to different drought stress conditions [14–16]. Previous studies have elucidated the molecular mechanisms of individual ecological factors, such as drought stress, in influencing saikosaponin synthesis and accumulation [17]. However, plants often experience various environmental stresses during their life cycles, and the impacts of these stresses can be highly variable [18]. When subjected to multiple environmental stressors, the interactions between stressors induce complex responses in plants [19]. Unlike responses to single stressors, the effects of combined stresses cannot be directly inferred from individual stress responses [20]. Upon perceiving combined stress stimuli, plants first activate signaling pathways involving secondary messengers, plant hormones, and transcription factors [21]. Multiple signals induced by different stresses converge onto common regulatory hubs, collectively modulating gene expression. This subsequently drives alterations in plant metabolism and phenotypic traits, ultimately leading to a distinct combinatorial response to the specific stress combination [22]. Studies on changes in artemisinin content in Artemisia annua under abiotic stress revealed that artemisinin levels increased under both salt stress and low-temperature stress, but decreased under drought stress. Notably, the most significant enhancement in artemisinin content occurred under low-temperature conditions [23]. Currently, there is a lack of understanding regarding the physiological responses and secondary metabolite synthesis mechanisms of B. chinense under complex, multi-stressor conditions.

Based on previous research, this study proposes the following hypothesis: the synthesis and accumulation of saikosaponins in B. chinense exhibit a “short-term promotion effect” regulated by multiple ecological factors. The “short-term promotion effect” refers to the rapid, transient upregulation of saikosaponin biosynthesis in B. chinense triggered by specific environmental stressors (e.g., drought combined with high temperature). This rapid increase is an adaptive response where the plant boosts the production of these key secondary metabolites over a relatively brief period to enhance stress tolerance and facilitate survival under challenging conditions. To explore this, a study was conducted to investigate the molecular ecological mechanism of saikosaponin formation in B. chinense under combined two-factor stress involving drought and temperature. The study’s overall framework is depicted in Fig. 1, which illustrates the experimental setup with drought and temperature stress treatments, including low temperature (15 °C) and high temperature (35 °C). These temperature values were selected based on prior assessments of the ecological suitability and temperature tolerance of B. chinense. The study analysed the changes in endogenous hormone signaling, transcription of key genes involved in saikosaponins synthesis, and saikosaponin contents to establish correlations among environmental factors (drought and temperature), genetic factors (gene expression), and phenotypic responses (hormones and saikosaponins). This approach facilitated the construction of a molecular ecological regulatory network for saikosaponins synthesis in B. chinense under combined two-factor stress. The findings aim to address bottlenecks in ecological planting by improving the quality of B. chinense medicinal materials and providing a scientific foundation and technical support for standardising the ecological cultivation of B. chinense to ensure consistent medicinal material quality.

Fig. 1 — The overall concept of the study on *Bupleurum chinense* under combined stress

Results

The effect of two-factor (drought and temperature) combined stress on the plant height and root length of B. chinense

The effects of two-factor (drought and temperature) combined stress on plant height and root length of B. chinense are shown in Fig. 2. The effects of the different stress conditions on plant height and root length were essentially the same. DL, DM, and DH inhibited the increases in plant height and root length. The difference is that DH had the strongest inhibitory effect, followed by DL, while DM had the weakest inhibitory effect. This indicates that combined stress had a stronger effect on the growth of B. chinense than single-factor stress. Adverse environments, such as high temperatures, droughts, and low temperatures, are not conducive to the growth and development of B. chinense. Additionally, it is worth noting that in terms of the impact on root length, the combined stresses had a greater effect than single drought stress, which may be related to the plant’s ability to adapt to environmental stress.

The effect of two-factor (drought and temperature) combined stress on the physiological indices of B. chinense

The effects of combined two-factor (drought and temperature) stress on the physiological indices of B. chinense are shown in Fig. 3. After the start of the combined drought and temperature stress, the MDA content in all treatment groups of B. chinense was generally higher than that in CK, and the degree of stress followed the trend: DH > DM > DL > CK (Fig. 3A). The increase in MDA content was most significant in DH and DM, and from days 4 to 16 of treatment, it remained much higher than that in CK, indicating that B. chinense experienced significant oxidative damage under DH and DM. For DL, on day 4 of treatment, the MDA content was lower than that in CK. After day 4, the MDA content in DL began to increase compared to CK, but the increase was much lower than that observed in DM and DH.

The effects of combined drought and temperature stress on the content of osmoregulatory substances in B. chinense are shown in Fig. 3B, C and D. Except for DM on day 16 of treatment, the soluble protein content in each treatment group showed a certain increase compared to CK, reaching its maximum value on day 12 of treatment. Under short-term combined stress, the soluble sugar content increased significantly, with the most pronounced changes observed in DL. Additionally, the proline content in each treatment group significantly increased compared to CK from days 8 to 16 of treatment. Overall, soluble sugar showed a significant improvement in the early stages of combined stress, whereas soluble protein and proline exhibited significant improvement in the later stages. This indicates that soluble sugars play an important role in maintaining osmotic balance during the early stages of combined stress, while soluble proteins and proline are critical for maintaining osmotic balance in the later stages. Soluble sugar, soluble protein, and proline complemented each other and cooperated throughout the entire combined stress process, jointly promoting the normal growth and development of B. chinense.

The effects of combined drought and temperature stress on the activities of protective enzymes in B. chinense are shown in Fig. 3E and F. The activity of SOD in both DM and DH exhibited an “M”-shaped trend, reaching two maximum values on days 4 and 12 of treatment. Among these, the activity of SOD in DH significantly increased by 20.71% and 61.49% compared to CK, and SOD activity in DM significantly increased by 19.62% and 34.18% compared to CK. Changes in POD activity in the different treatment groups followed the trend: DH > DL > DM > CK. POD activity in DH and DL was significantly higher than that in CK in the early (day 4) and late (days 12 and 16) stages of treatment. POD activity in DM did not change significantly after the start of combined stress but was significantly higher than CK on day 12 of treatment.

The effect of two-factor (drought and temperature) combined stress on the key enzyme activities in the terpenoid synthesis pathway

The changes in the activities of five enzymes in the terpenoid synthesis pathway—HMGR, FPS, SQS, SQE, and β-AS—under combined drought and temperature stress are shown in Fig. 4. On day 4 of treatment, HMGR activity in DM, DH, and DL was higher than that in CK. HMGR activity in DM and DH showed an initial increase followed by a decrease, while HMGR activity in DL exhibited a slow upward trend. This indicates that short-term drought with medium temperature, high temperature, and low temperature can stimulate the up-regulation of HMGR activity, with a stronger up-regulation observed under combined stress than under single stress.

Fig. 4 — The effect of two-factor (drought and temperature) combined stress on key enzyme activities in terpenoid synthesis pathways of *B. chinense*. A Changes in HMGR activity of *B. chinense* under two-factor combined stress. B Changes in FPS activity of *B. chinense* under two-factor combined stress. C Changes in SQS activity of *B. chinense* under two-factor combined stress. D Changes in SQE activity of *B. chinense* under two-factor combined stress. E Changes in β-AS activity of *B. chinense* under two-factor combined stress. Data are expressed as the mean ± SD (standard deviation, n = 3). The different lowercase letters indicate significant differences between treatments (p < 0.05, Duncan’s Single-factor variance analysis)

The activity of FPS in DM, DH, and DL was higher than that in CK from the onset of combined stress (except for DL on day 16). The increase in FPS activity in DM and DH was greater than that in DL. FPS activity reached its maximum value in DM on day 12 and in DH on day 16, indicating that FPS was more sensitive to the stress effects caused by drought in high-temperature environments.

SQS activity was highest in DL, followed by DM. Except for day 8 of treatment, all other time points showed higher SQS activity than CK, indicating that drought with low-temperature conditions can stimulate an increase in SQS activity for a prolonged period.

SQE activity in each treatment group was higher than that in CK (except for DL on day 12). SQE activity significantly increased in DH from days 8 to 12 of treatment, with increases ranging from 58.57% to 92.44% compared to CK.

β-AS activity continued to increase in DL and DM, while it reached its maximum value on day 8 of treatment and subsequently decreased in DH. This indicates that short-term drought in a high-temperature environment can significantly increase β-AS activity but cannot sustain the improvement effect long term. However, droughts with low temperatures and in medium-temperature environments sustained and enhanced β-AS activity over a prolonged period, leading to a continuous increase in its activity.

The effect of two-factor (drought and temperature) combined stress on the saikosaponins content in B. chinense roots

The changes in the content of saikosaponins—saikosaponin a, saikosaponin d, saikosaponin c, saikosaponin e, and saikosaponin f—in B. chinense under combined drought and temperature stress are shown in Fig. 5. The changing trends of total saikosaponins, saikosaponin a, and saikosaponin d in each treatment group were consistent. The contents of total saikosaponins, saikosaponin a, and saikosaponin d in DL were 2.93, 1.27, and 1.02 mg/g on day 4 of treatment, significantly lower than those in CK, representing decreases of 13.60%, 12.07%, and 11.21%, respectively, compared to CK. The contents of total saikosaponins, saikosaponin a, and saikosaponin d in DL continued to increase from day 4 to day 16 of treatment and reached maximum values on day 16; however, their levels always remained lower than those in CK.

Fig. 5 — The effect of two-factor (drought and temperature) combined stress on key terpenoid compound saikosaponins content of *B. chinense*. A Changes in saikosaponin a content of *B. chinense* under two-factor combined stress. B Changes in saikosaponin c content of *B. chinense* under two-factor combined stress. C Changes in saikosaponin d content of *B. chinense* under two-factor combined stress. D Changes in saikosaponin e content of *B. chinense* under two-factor combined stress. E Changes in saikosaponin f content of *B. chinense* under two-factor combined stress. F Changes in saikosaponins content of *B. chinense* under two-factor combined stress. Data are expressed as the mean ± SD (standard deviation, n = 3). The different lowercase letters indicate significant differences between treatments (p < 0.05, Duncan’s Single-factor variance analysis)

The contents of total saikosaponins, saikosaponin a, and saikosaponin d in DM consistently increased and were higher than those in CK throughout the experiment. On day 12 of treatment, their levels in DM reached 5.16, 2.31, and 1.92 mg/g, respectively, representing the greatest increases compared to CK, with percentages of 19.29%, 19.23%, and 16.06%, respectively. However, saikosaponin a, and saikosaponin d contents in DM decreased on day 16 and were significantly lower than those in CK.

The contents of total saikosaponins, saikosaponin a, and saikosaponin d in DH reached 4.52, 2.07, and 1.54 mg/g on day 4 of treatment, significantly higher than those in DM, CK, and DL, with increases of 40.82%, 32.76%, and 36.97%, respectively, compared to CK. These contents significantly increased in the short term under DH. However, from days 4 to 12 of treatment, the contents of total saikosaponins, saikosaponin a, and saikosaponin d in DH decreased steadily. The contents of saikosaponin c, e, and f were relatively low, and their content changes in each treatment group did not show any obvious trends or regular patterns.

Overall, combined stress had varying effects on saikosaponin levels in B. chinense. Under drought with low temperature, saikosaponin content steadily increased but remained lower than in CK. Drought conditions promoted saikosaponin accumulation over a limited period, but prolonged drought stress caused a decline. Drought with high temperature caused more severe stress. In the early stages of high-temperature drought, saikosaponin content increased significantly, but it decreased thereafter due to the intense stress conditions.

Transcript abundance analysis of DEGs of B. chinense roots treated with two-factor (drought and temperature) combined stress

The CK, DL, DM, and DH samples collected on day 4 of treatment were used for transcriptome sequencing and data analysis, based on changes in physiological indices, functional enzyme activities, and saikosaponin content. A total of 492,375,614 raw reads were generated from the 12 samples, and 441,589,644 valid reads were obtained after removing sequencing connectors and unqualified sequences. The proportion of valid reads was 89.69%, while the data volume of valid reads was 63.54 G. The percentages of Q20 and Q30 in the 12 samples were greater than 94.84% and 91.73%, respectively. The GC content ranged from 42.67% to 43.15% (Supplementary Tab. S2). Overall, the sequencing quality was high, and these data were suitable for subsequent analyses.

Through transcriptome analysis, a total of 68,014 unigenes were obtained. DL vs. CK had 4,289 differentially expressed unigenes (1,774 up-regulated and 2,515 down-regulated unigenes), DH vs. CK had 10,105 differentially expressed unigenes (4,196 up-regulated and 5,909 down-regulated unigenes), and DM vs. CK had 2,532 differentially expressed unigenes (1,139 up-regulated and 1,393 down-regulated unigenes) (Fig. 6A). This indicates that B. chinense underwent more pronounced transcriptional changes under drought with high temperature.

Fig. 6 — Transcript analysis of differentially expressed genes (DEGs) of *B. chinense* treated with two-factor (drought and temperature) combined stress. A The number of up-regulated and down-regulated genes with differential expression (DL vs. CK, DH vs. CK and DM vs. CK). B Upset plot of the number of DEGs annotation in six databases. C Distribution of homologous species based on NR annotation. D Venn diagram of all down-regulated DEGs and all up-regulated ones from the transcriptome and data filtering process for weighted gene co-expression network analysis (WGCNA). E The hierarchical dendrogram displays co-expression modules revealed by WGCNA. F Heatmap of co-expression genes network of in different modules by WGCNA. G Correlation between seven color modules and samples by WGCNA

All 38,548 unigenes were annotated in at least one database (NR, Swissprot, Pfam, eggNOG, GO, or KEGG), resulting in an annotation proportion of 100%. Among these, 19,512 unigenes (50.62%) were annotated in all six databases (Fig. 6B). In all 6 databases, the numbers of unigenes annotated, from high to low, were as follows: NR (38,548, 100.00%) (Supplementary Tab. S3), eggNOG (36,540, 94.79%) (Supplementary Tab. S4), GO (32,760, 84.98%) (Supplementary Tab. S5), Pfam (29,620, 76.84%) (Supplementary Tab. S6), Swissprot (27,469, 71.26%) (Supplementary Tab. S7), and KEGG (24,758, 64.23%) (Supplementary Tab. S8). The distribution of homologous species after NR annotation is shown in Fig. 6C. Most unigenes were annotated as Daucus carota (26,098, accounting for approximately 67.70%). Other annotated species included Solanum lycopersicum (3,449, accounting for approximately 8.95%), Quercus suber (832, accounting for approximately 2.16%), and Vitis vinifera (346, accounting for approximately 0.90%).

The Venn diagram of differentially expressed unigenes between treatment groups highlighted the varying effects of treatments on the transcription levels of B. chinense (Fig. 6D). After screening all 68,014 unigenes using the key parameters shown in Figs. 7D, 25,642 unigenes were selected for WGCNA.

Fig. 7 — Functional analysis of transcriptome WGCNA key modules of *B. chinense* treated with two-factor (drought and temperature) combined stress. A Changes in the content of growth and development-related hormones and environment response-related hormones in samples of *B. chinense* treated with two-factor (drought and temperature) combined stress after 4 days. From left to right, they are 3-hydroxybutyric acid, indole-3-carboxylic acid, indole-3-carboxaldehyde, isopentenyl adenosine, N-jasimonic acid isoleucine, 1-aminocyclopropanecarboxylic acid, dihydrojasmonic acid, jasmonic acid, salicylic acid and abscisic acid, respectively (n = 6). B Module–trait relationships between modules (seven color modules, in left) and traits (physiological indices, plant hormones, functional enzymes and saikosaponins, in bottom). C Expression patterns of unigenes in the selected turquoise modules. D GO analysis of all identified unigenes in the selected turquoise modules. E KEGG analysis of all identified unigenes in the selected turquoise modules. The circle diagram is viewed from the outside and is divided into circle a, b, c, and d. Circle a is the pathway enrichment classification, and the number of genes outside the circle is on the scale. Circle b represents the number of genes in this category in the background. Circle c represents the number of genes in the foreground, with darker colors indicating higher importance. Circle d represents the Rich Factor values for each pathway enrichment category

The hierarchical dendrogram (Fig. 6E) illustrates the overall partitioning and effectiveness of WGCNA modules. The first section is a gene clustering tree based on a topological overlap measure, representing clustering relationships of each gene. The second section uses a dynamic hybrid pruning algorithm to divide genes into modules, optimising module partitioning results. Different colours represent distinct modules.

A heat map of the co-expressed gene network (Fig. 6F) shows the partitioning effect of WGCNA modules. The left and upper sides display results from the symmetrical system-clustering tree and gene modules, while the lower-right heat map area represents gene dissimilarity, where smaller values indicate darker colours.

Using the module and sample correlation heat map (Fig. 6G), the most relevant modules for the samples were selected. Further data mining and discussion were conducted on module functional enrichment analysis results, as well as the core network and core genes within the module.

Combined stress response-related modules obtained by weighted co‑expression network analysis

Plant hormones play a critical role in responding to abiotic stress [24]. To identify genes associated with drought with high temperature response modules, we analysed the content changes of 10 endogenous hormones, including those related to growth and development and those related to environmental response, across different treatment groups of B. chinense. The results are shown in Fig. 7A.

Plant hormones involved in growth and development include 3-hydroxybutyric acid, indole-3-carboxylic acid, indole-3-carboxaldehyde, isopentenyl adenosine, N-jasmonic acid isoleucine, and 1-aminocyclopropanecarboxylic acid. Indole-3-carboxaldehyde exhibited significant changes under combined stress, with DL, DM, and DH being 2.97-, 1.83-, and 5.59-fold higher, respectively, than CK. Isopentenyl adenosine showed the highest content in DL, with DL, DM, and DH being 5.99-, 1.70-, and 3.49-fold higher, respectively, than CK.

Plant hormones related to environmental responses include dihydrojasmonic acid, jasmonic acid, salicylic acid, and abscisic acid. Dihydrojasmonic acid and jasmonic acid were only elevated in DM, reaching 1.45- and 1.38-fold higher than CK, respectively. Salicylic acid content was higher than CK only in DH, at 3.78-fold. Abscisic acid showed the most significant changes under combined stress, with DL, DM, and DH being 12.90-, 6.38-, and 16.27-fold higher, respectively, than CK. Among the plant hormones, abscisic acid exhibited the most pronounced changes under combined stress.

Using pairwise correlation analysis with gene expression, WGCNA was employed to identify candidate genes highly associated with physiological indices, plant hormones, functional enzymes, and saikosaponins in B. chinense (Fig. 7B). Seven gene modules of the co-expression network were identified: brown (3,276 target genes), yellow (1,541 target genes), turquoise (12,753 target genes), red (805 target genes), blue (5,659 target genes), green (1,057 target genes), and grey (551 target genes) (Supplementary Tab. S9). At p < 0.05, correlation coefficient values indicated that the turquoise module was significantly positively associated with physiological indices (including MDA, soluble protein, SOD, and POD), plant hormones (including indole-3-carboxaldehyde, salicylic acid, and abscisic acid), functional enzymes (including HMGR, FPS, and SQE), and saikosaponins (including saikosaponins a, c, d, and f). Therefore, the turquoise module was considered the combined stress-response-related module for further analysis.

The expression pattern of unigenes in the selected turquoise module is shown in Fig. 7C. Unigenes in DH were predominantly up-regulated, significantly distinguishing them from those in DM, DL, and CK. GO and KEGG enrichment analyses of unigenes in the turquoise module revealed significant enrichment in GO categories of biological processes, cellular components, and molecular functions (Fig. 7D) (Supplementary Tab. S10) and KEGG categories of Cellular Processes, Environmental Information Processing, Genetic Information Processing, and Metabolism (Fig. 7E) (Supplementary Tab. S11).

DEGs involved in the saikosaponins and abscisic acid biosynthesis under combined stress

Based on the biosynthesis pathways of saikosaponins and abscisic acid, combined with KEGG pathway analysis, we created a pathway view heatmap illustrating saikosaponins and abscisic acid biosynthesis and candidate gene expression in B. chinense under combined stress (Fig. 8).

Fig. 8 — Pathway view heatmap of saikosaponins and abscisic acid and candidate gene expressions in *B. chinense* treated with two-factor (drought and temperature) combined stress

Among these DEGs, 14 genes were identified in the mevalonate (MVA) pathway, including two AACT genes, three HMGS genes, five HMGR genes, one MK gene, two PMK genes, and one MVD gene. Eleven genes were identified in the methylerythritol phosphate (MEP) pathway, including three DXS, one DXR, one MCT, one CMK, one MDS, two HDS, and two HDR genes. These DEGs, including IDI (three genes), GPPS (eight genes), FPS (two genes), SQS (one gene), SQE (four genes), β-AS (two genes), P450s (168 genes), and UGT (86 genes), were identified as being involved in the transformation of oleanane-type saikosaponins. Additionally, DEGs including BCH (three genes), VED (two genes), ZEP (one gene), NCED (two genes), and ABA2 (eight genes) were identified as being involved in abscisic acid biosynthesis.

The pathway view heatmap further illustrates the saikosaponin and abscisic acid contents under different combined stress treatments. Following differential gene expression, the saikosaponin content ranked as DH > DM > CK > DL, indicating that drought with high temperature stress strongly stimulate saikosaponin synthesis. Meanwhile, abscisic acid content followed the order DH > DL > DM > CK, suggesting that under combined stress, B. chinense synthesises higher abscisic acid levels to adapt to environmental changes.

Analysis of DEGs of transcription factors related to saikosaponins and abscisic acid biosynthesis under combined stress

Transcription factors play a crucial role in bridging plant responses to environmental signals and the synthesis of bioactive compounds. They are integral to regulating secondary metabolite synthesis in plants [25]. In this study, 589 genes belonging to six major transcription factor families responsive to combined stress were identified. These included 104 basic/helix-loop-helix transcription factors (bHLH), 103 ethylene-responsive factors (ERF), 43 homeodomain-leucine zipper transcription factors (HD-ZIP), 87 MYB transcription factors (MYB), 66 NAC domain transcription factors (NAC), and 186 WRKY transcription factors (WRKY). A heatmap and box plot of the DEGs annotated as different transcription factors are shown in Fig. 9A.

Fig. 9 — Analysis of DEGs of transcription factors related to saikosaponins and abscisic acid in *B. chinense* treated with two-factor (drought and temperature) combined stress. A Heatmap and box plot of DEGs annotated as different transcription factors. B Correlation network plot of transcription factors, key genes involved in terpenoid synthesis and terpenoid bioactive components

The active expression of various transcription factors in the DH group may be linked to secondary metabolic activity in B. chinense. Furthermore, a regulatory network plot was constructed to depict the relationship between differential transcription factors, terpene biosynthesis pathway genes, and key terpenoid secondary metabolites (Fig. 9B). In this plot, the pink lines indicate positive correlations, the blue lines indicate negative correlations, and the size of the dots (representing specific genes) corresponds to the number of genes associated with other genes. This analysis revealed that saikosaponins synthesis is regulated by multiple transcription factors and functional genes and that abscisic acid and saikosaponins have distinct regulatory networks. These findings suggest that B. chinense adapts to combined stress by activating multiple transcription factors to regulate key functional genes, ultimately enabling the accumulation of saikosaponins in the roots to respond to complex environmental changes.

Validation by qRT-PCR

To validate the transcriptome data for B. chinense under combined stress conditions, qRT-PCR was performed to analyse the genes involved in saikosaponins biosynthesis. Triplicate tests were conducted on the following genes: AACT2 (TRINITY_DN9016_c0_g2), DXS3 (TRINITY_DN4358_c0_g1), IDI1 (TRINITY_DN12228_c0_g1), SQE1 (TRINITY_DN14245_c0_g1), P450-21 (TRINITY_DN5284_c0_g1), P450-18 (TRINITY_DN3156_c0_g1), P450-93 (TRINITY_DN7987_c0_g1), and UGT-56 (TRINITY_DN4358_c0_g1). The results demonstrated that the expression trends of the eight DEGs across different stress groups were consistent with the transcriptome sequencing results (Fig. 10).

Fig. 10 — Comparison of qPCR results with transcriptome TPM values. A ACCT2: TRINITY_DN9016_c0_g2. B DXS3: TRINITY_DN4358_c0_g1. C IDI1: TRINITY_DN12228_c0_g1. D SQE1: TRINITY_DN14245_c0_g1. E P450-21: TRINITY_DN5284_c0_g1. F P450-18: TRINITY_DN3156_c0_g1. G P450-93: TRINITY_DN7987_c0_g1. H UGT-56: TRINITY_DN5759_c0_g1. Data are expressed as the mean ± SD (standard deviation, n = 3)

Discussion

The impact of environmental stress on the growth and physiological changes of B. chinense

The effects of environmental factors on plant growth and development are multifaceted and dynamic [26]. Changes in plant growth and phenotypic differences under varying environmental factors are among the most intuitive manifestations of plant responses to environmental stress [27]. In nature, combined stress from multiple ecological factors induces significant changes in biological growth and metabolic processes [28]. When medicinal plants experience combined effects, these factors often enhance or superimpose their individual impacts, promoting mutual amplification [29].

Plants frequently encounter various interacting stresses that exacerbate unfavourable conditions, ultimately leading to more severe damage [30]. This study demonstrated that two-factor (drought and temperature) combined stress inhibited the growth and development of B. chinense. Among these, drought combined with high temperature exerted the strongest inhibitory effect on plant height and root length, followed by drought with low temperature. Single-factor stress (drought) had the weakest inhibitory effect, indicating that multi-factor combined stress has a more pronounced impact on B. chinense growth than single-factor stress.

Plants adapt to stressful environments, such as high or low temperatures and drought, by regulating osmoregulatory substances, antioxidant enzyme systems, secondary metabolite synthases, and plant hormones [31]. MDA, a recognised indicator of abiotic stress, is the final product of membrane lipid peroxidation [32]. In this study, short-term drought stress elicited the most rapid response in B. chinense. Over prolonged stress durations, two-factor combined stress caused more severe oxidative damage compared to single-factor stress. This indicates that oxidative damage in B. chinense is influenced by both the duration and severity of stress.

SOD and POD are essential antioxidant enzymes that help plants resist oxidative damage and maintain normal growth. In this study, SOD activity was significantly higher in DM and DH groups compared to CK. Similarly, POD activity in DM, DH, and DL groups was consistently higher than in CK, highlighting the active role of these enzymes in antioxidant defence across different stress environments.

Osmoregulatory substances are vital physiological components that enable plants to adapt to adverse environments [33]. Throughout the stress stages, the contents of soluble sugar, soluble protein, and proline were consistently higher in the treatment groups compared to CK, except for soluble sugar in DM on day 16. Among these, proline content exhibited the most significant changes, indicating that soluble sugars, soluble proteins, and proline collectively and actively respond to environmental stress across all stages.

Medicinal plants cope with environmental stress by synthesising secondary metabolites

Under harsh environmental conditions, plants regulate the expression of resistance genes, synthesise and accumulate secondary metabolites, and enhance their resistance to diseases, insects, and environmental stress [34, 35]. The results of this study demonstrated that combined stress affects the synthesis of secondary metabolites by regulating the activity of key enzymes and gene expression in secondary metabolite pathways. Among the saikosaponin components in B. chinense, saikosaponins a and d had the highest content, significantly surpassing other saikosaponins. Saikosaponins a and d are the predominant bioactive constituents in B. chinense due to their higher natural abundance and established pharmacological roles. Saikosaponins a significantly contributes to anti-inflammatory and hepatoprotective effects, while Saikosaponins d shows potent antiviral activity. In contrast, saikosaponin c, e, and f occur at substantially lower concentrations with comparatively limited documented bioactivity.

Drought with high-temperature combined stress exerted a significant impact on the content of saikosaponins, saikosaponin a, and saikosaponin d in B. chinense (p < 0.05). The trends in saikosaponin content under drought with high-temperature combined stress were consistent across saikosaponins, saikosaponin a, and saikosaponin d. Under DH conditions, their content was highest during the early treatment stages due to favourable environmental conditions. The content of saikosaponins, saikosaponin a, and saikosaponin d increased sharply, surpassing the levels observed in CK. However, as treatment time progressed, their content began to decline, ultimately falling below that of CK. The observed decline in saikosaponin accumulation under prolonged stress likely originates from ROS signaling attenuation, which suppresses both ROS-scavenging SOD activity and saikosaponin-biosynthetic enzymes (SQS, SQE, β-AS), creating a metabolic bottleneck in the biosynthesis pathway. Under DM conditions, the contents of saikosaponins, saikosaponin a, and saikosaponin d significantly increased from the beginning of treatment (p < 0.05) and remained consistently higher than CK. However, over time, their content began to decrease and eventually fell below the CK levels. In contrast, the changes observed under DL conditions were distinct. From the beginning of the treatment, saikosaponins, saikosaponin a, and saikosaponin d content in DL increased slowly over time but remained lower than CK at all treatment stages. Studies on Panax ginseng have demonstrated that drought stress inhibits root biomass accumulation but significantly enhances ginsenoside biosynthesis [36]. Additionally, moderate cold stress promotes ginsenoside production [37]. The study revealed that drought stress levels achieved an equilibrium between the relatively favorable growth patterns and enhanced expression of bioactive compounds in licorice (Glycyrrhiza) species [38]. However, the responsive mechanisms of medicinal plant to combined stress conditions remain elusive.

Overall, short-term drought with high-temperature stress significantly increased saikosaponin content in B. chinense. In comparison, single drought stress sustained long-term growth of saikosaponin content. Furthermore, the changes in key enzyme activities—HMGR, FPS, SQS, SQE, and β-AS—aligned with the trends in saikosaponin content under DM, DH, and DL conditions. This finding underscores that B. chinense regulates key enzyme activities to modulate saikosaponin synthesis as a strategy to cope with environmental stress.

Possible mechanism of the “short-term promotional effect” of two-factor combined stress on the synthesis of saikosaponins in B. chinense

Through transcriptome analysis, we found that the number of DEGs was highest under DH conditions, followed by DL conditions, and lowest under DM conditions. Notably, the number of down-regulated DEGs exceeded the number of up-regulated DEGs, indicating that gene expression in B. chinense was significantly affected by two-factor combined stress and that multiple biological processes were inhibited. GO enrichment analysis revealed that numerous biological functions were regulated to combat abiotic stress, while KEGG enrichment analysis identified multiple regulated metabolite pathways. Genes within the MAPK signal transduction pathway were significantly regulated under all three combined stress conditions (DM, DH, and DL), facilitating the transduction of abiotic stress signals in B. chinense.

Similarly, many genes involved in metabolite pathways related to plant hormones and saikosaponin synthesis were regulated, influencing secondary metabolite synthesis in B. chinense. Using targeted metabolomics technology, we analysed changes in 10 endogenous plant hormones—comprising both growth- and development-related hormones and environmental response-related hormones—across the treatment groups. These plant hormones exhibited distinct trends in response to abiotic stress, reflecting their diverse functions in plants [39].

While several hormones played significant roles in the environmental stress response, their regulation varied across abiotic stress conditions. Key hormones, including abscisic acid, indole-3-carboxaldehyde, isopentenyl adenosine, and 1-aminocyclopropanecarboxylic acid, were positively regulated during all combined stress conditions (DM, DH, and DL). Conversely, other hormones, such as salicylic acid and jasmonic acid, showed selective regulation under different stress environments. Specifically, DH conditions stimulated an increase in salicylic acid content, whereas DM conditions promoted an increase in jasmonic acid content.

These findings suggest that B. chinense may selectively regulate certain plant hormone levels to mitigate damage and adapt to varying abiotic stresses. By fine-tuning hormone levels, B. chinense appears to optimise its physiological responses, promoting survival and adaptation in complex environmental conditions.

WGCNA was used to identify candidate genes highly associated with physiological indices, plant hormones, functional enzymes, and saikosaponins in B. chinense. WGCNA revealed that the turquoise module (containing 12,753 target genes) was positively correlated with physiological indices, plant hormones, functional enzymes, and saikosaponins and is considered a combined stress response-related module. Using the target genes of the turquoise module, combined with the biosynthesis processes of saikosaponins and abscisic acid and KEGG pathway analysis, we generated a pathway view heatmap illustrating saikosaponins, abscisic acid, and candidate gene expression under two-factor (drought and temperature) combined stress.

The heatmap included 14 genes involved in the MVA pathway, 11 genes involved in the MEP pathway, 14 genes involved in oleanane-type saponin synthesis, and 14 genes involved in abscisic acid synthesis. These results indicate that gene expression patterns align with final metabolite content, suggesting that two-factor (drought and temperature) combined stress can enhance secondary metabolite synthesis by regulating gene expression, enabling plants to adapt to environmental changes. Additionally, we speculate that the “short-term promotional effect” of combined drought with high temperature stress on saikosaponin content in B. chinense results from the activation of related secondary metabolite pathway genes. As illustrated in Fig. 8, genes of the MVA pathway (AACT2, PMK1, PMK2) and MEP pathway (DXS3, CMK, MDS, HDS1, HDS2, HDR1, HDR2) exhibited significant upregulation under combined drought and high temperature stress compared to CK. This transcriptional reprogramming drove the redirection of metabolic flux toward saikosaponin biosynthesis.

Transcription factors regulate plant responses to environmental signals and the synthesis of bioactive compounds, including secondary metabolites [40]. They play critical roles in controlling the expression of membrane proteins, antioxidant enzymes, osmoregulatory synthases, and secondary metabolites [41]. The MYB transcription factor family regulates primary and secondary metabolism in plants, responding to biotic and abiotic stresses and influencing growth and development [42]. ZF-HD transcription factors are involved in plant growth, development, and responses to abiotic stress and plant hormones [43]. The WRKY family participates in plant hormone and abiotic stress signal transduction pathways, inducing the expression of various hormone- and stress-related genes [44].

In this study, 589 genes belonging to six major transcription factor families responsive to combined stress were identified, including bHLH, ERF, HD-ZIP, MYB, NAC, and WRKY. A regulatory network was constructed linking differential transcription factors, terpenoid biosynthesis pathway genes, and key terpenoid secondary metabolites. Our analysis revealed that saikosaponin synthesis is regulated by multiple transcription factors and functional genes, while abscisic acid and saikosaponins follow distinct regulatory networks. Under combined drought and high temperature stress, ABA accumulation in plants triggers its binding to PYR/PYL receptors, leading to PP2C phosphatase inhibition and subsequent SnRK2 kinase cascade activation. Furthermore, ABA potentially induces the expression of transcription factors (e.g., MYB, WRKY), which are documented to bind promoters of terpenoid biosynthetic genes and redirect metabolic flux [45, 46]. Consequently, ABA may orchestrate saikosaponin biosynthesis via a hierarchical ‘receptor–transcription factor–structural gene’ regulatory axis, though experimental validation of this cascade is necessary. These findings may suggest that B. chinense adapts to combined stress by activating multiple transcription factors to regulate key functional genes, resulting in the rapid accumulation of saikosaponins in roots and producing the “short-term promotional effect” observed under combined stress conditions.

While this study investigated the impact of combined drought and temperature stress on saikosaponin content and explored associated molecular and ecological mechanisms in B. chinense, several limitations should be acknowledged. Firstly, the imposed stress duration was relatively short-term, while demonstrating an initial rapid promotional effect, may not fully reflect long-term adaptive responses or the potential for metabolic exhaustion under sustained stress. Secondly, the controlled pot-based experimental design inherently restricts root growth dynamics and soil moisture distribution compared to natural field soil; the simulated drought and fixed temperature regimes also simplify the complex, fluctuating nature of these stresses in agricultural or wild settings. Consequently, these factors limit direct extrapolation, as results could potentially diverge under real-world field conditions where additional variables such as soil heterogeneity, microbial interactions, seasonal weather patterns, and competing vegetation contribute significant variability. In the future, more extensive experiments need to be carried out to verify the impact of ecological factors on the physiology and secondary metabolism of medicinal plants.

Conclusion

We investigated changes in saikosaponin content in B. chinense under the combined stress of two dominant environmental factors (drought and temperature) and their potential molecular ecological mechanisms. DL, DM, and DH inhibited plant height and root length, with DH exerting the strongest inhibitory effect. MDA content was consistently higher in all treatment groups compared to CK, with the degree of stress following the trend: DH > DM > DL > CK. B. chinense adapted to combined stress by regulating osmoregulatory substances and protective enzyme activities.

Five enzymes in the terpenoid synthesis pathway (HMGR, FPS, SQS, SQE, and β-AS) displayed positive changes under combined stress, supporting the synthesis of saikosaponins. Saikosaponin content, including saikosaponin a and saikosaponin d, increased significantly in DH during the early treatment stages, reaching 4.52, 2.07, and 1.54 mg/g, respectively, on day 4—an increase of 40.82%, 32.76%, and 36.97% compared to CK. Abscisic acid levels also increased significantly under combined stress, with DL, DM, and DH showing 12.90-, 6.38-, and 16.27-fold increases, respectively, over CK.

High-quality transcriptome sequencing revealed active changes in gene expression profiles in B. chinense under combined stress conditions. The turquoise module (12,753 target genes), identified via WGCNA, was highly correlated with physiological indices, plant hormones, functional enzymes, and saikosaponins. A pathway view heatmap of saikosaponins, abscisic acid, and candidate gene expression under combined stress demonstrated that drought with high temperature stress strongly stimulate saikosaponin synthesis. Saikosaponin synthesis is regulated by multiple transcription factors and functional genes, while abscisic acid and saikosaponins follow distinct regulatory pathways.

Our results reveal that the synthesis and accumulation of saikosaponins exhibit a “short-term promotional effect” regulated by multiple ecological factors, elucidating the molecular ecological mechanism of rapid saponin accumulation. These findings provide a scientific basis and potential technical support for ecological regulation strategies aimed at producing high-quality B. chinense.

Materials and methods

Experimental materials and treatment methods

One-year-old B. chinense DC. plants were obtained in April 2023 from cultivated populations at Huang Village, He Village Township, Song County, Luoyang City, Henan Province (34°9′ N, 111°59′ E). The species information of the obtained plants was identified by Professor Chengming Dong and confirmed to be the Apiaceae plant Bupleurum chinense DC. These plants were transported to the Medicinal Botanical Garden at Henan University of Chinese Medicine and transplanted into cylindrical plastic pots (10 cm in diameter, 20 cm in height). The pots were filled with an equal mixture of nutrient soil and native soil, with one plant per pot. Plants with uniform growth and no visible signs of pests or disease were selected for the experiment. Daily management included standardised watering to acclimate the plants to the Medicinal Botanical Garden environment before initiating the combined stress experiment. The plants were divided into four treatment groups: (1) Control group (CK): Normal watering with 25 °C; (2) Drought with low-temperature group (DL): 15% PEG6000 solution with 15 °C; (3) Drought with medium-temperature group (DM): 15% PEG6000 solution with 25 °C; (4) Drought with high-temperature group (DH): 15% PEG6000 solution with 35 °C. To ensure precise temperature control, each group was placed in a separate artificial climate chamber. Drought stress was simulated using a 15% PEG6000 solution. At the start of the experiment, each potted plant in the DL, DM, and DH groups was pretreated with 85 mL of the PEG6000 solution, and then 30 mL PEG6000 solution was poured every 2 days. Plants in the CK group were watered with an equivalent amount of fresh water throughout the experiment. A schematic of the experimental design and schedule for the combined drought and temperature stress treatments is presented in Fig. 11.

Fig. 11 — Experimental schedule for two-factor (drought and temperature) combined stress on *Bupleurum chinense*

Samples were collected on days 4, 8, 12, and 16, for a total of four sampling points. After each sampling, the B. chinense plants were cleaned, and their height and root length were measured using a ruler. Portions of the cleaned roots were dried in an oven at 60 °C for 4 h to determine saikosaponin content. The remaining fresh samples were frozen in liquid nitrogen and stored in a − 80 °C freezer for subsequent molecular biology experiments.

Determination of physiological indices

The physiological indices measured in this study included malondialdehyde (MDA), osmoregulatory substances, and protective enzyme activity. MDA is a by-product of membrane lipid peroxidation, MDA reflects the extent of stress experienced by the plants. Osmoregulatory substances include soluble proteins, soluble sugars, and proline, which increase cellular fluid concentration, lower osmotic potential, and enhance stress adaptation. Protective enzymes, including superoxide dismutase (SOD) and peroxidase (POD), which possess antioxidant properties, were assessed for their roles in enhancing stress resistance. The methodology for processing plant samples followed previously reported procedures [14]. The content of these physiological indices in B. chinense was measured using a multimode microplate reader (VARIOSKAN LUX, Thermo Fisher Scientific, USA) at specific wavelengths tailored to each reaction solution.

Determination of key enzyme activities in the terpenoid synthesis pathway

The activities of five enzymes involved in the terpenoid biosynthesis pathway were measured: 3-hydroxy-3-methyl glutaryl coenzyme A reductase (HMGR), phenylene pyrophosphate synthase (FPS), squalene synthase (SQS), squalene epoxidase (SQE), and β-amyrin synthase (β-AS). These enzyme activities were assessed using specific ELISA kits purchased from Shanghai Kexing Trading Co., Ltd. (China). The item numbers for the kits were as follows: HMGR (F7968-B), FPS (F50123-B), SQS (F8048-B), SQE (F8036-B), and β-AS (F8047-B). The enzyme activities were determined according to the standard procedures outlined in the ELISA kit instructions.

Determination of saikosaponin content in B. chinense roots

Preparation of the sample extraction solution: Accurately weigh 0.4 g root sample powder of B. chinense root and place in a conical flask. Precisely add 10 mL of a 10% ammonia methanol solution (v/v), sonicated (power 800 W, frequency 40 kHz) for 40 min. After filtration and repeated extraction three times, the filtrates were combined and placed in an evaporating dish. The mixture was evaporated in a 50 °C water bath, and the residue was dissolved in methanol and diluted to a volume of 10 mL in a volumetric flask. The volumetric flask was shaken well and passed through a 0.22 μm filter membrane to obtain the sample extraction solution.

Preparation of saikosaponin standards solution: Accurately weigh saikosaponin a (B20146), saikosaponin c (B20149), saikosaponin d (B20150), saikosaponin e (B24458), and saikosaponin f (B20151) (Shanghai Yuanye Bio-Technology Co., Ltd, China) in 10 mL volumetric flasks, dissolve them in methanol, and prepare mixed standards solution with concentrations of 4.00, 3.98, 4.00, 4.00, and 4.01 mg/mL, respectively.

High-performance liquid chromatography (HPLC) analysis conditions: The analysis was performed using a high performance liquid chromatograph (e2695-2489, Waters, USA). The chromatographic column model is Agilent C18 (4.6 mm × 250 mm, 5 μm). Using water (solvent A)-acetonitrile (solvent B) as the mobile phase, gradient elution conditions: 0–5 min, 70% A; 5–10 min, 70–65% A; 10–20 min, 65–50% A; 20–25 min, 50–48% A; 25–40 min, 48–70% A. The detection wavelength is set to 200 nm, the chromatographic column temperature is set to 21 °C, and the injection volume is 20 µL.

The sample extraction solution and saikosaponin standard solutions were analysed according to the above HPLC conditions to obtain chromatograms of B. chinense roots and saikosaponin standards, and to compare the peak areas of the samples and the standards to calculate the content of saikosaponins (saikosaponins a, c, d, e, and f) in the B. chinense samples.

Transcriptome sequencing and data analysis

After analysing the physiological indices, functional enzyme activities, and saikosaponin content of different samples under combined two-factor (drought and temperature) stress, we selected the CK, DL, DM, and DH samples on the days of treatment for transcriptome sequencing and data analysis. RNA was extracted from B. chinense root samples using a Total RNA Purification Kit (LC Science, USA). The RNA purity was measured using a NanoPhotometer spectrophotometer (IMPLEN, USA). RNA concentration was accurately measured with a Qubit 2.0 fluorometer (Life Technologies, USA), and RNA integrity was evaluated using an Agilent 2100 bioanalyser (Agilent Technologies, USA). Construction of a cDNA library using high-quality total RNA. An Illumina NovaSeq 6000 platform (LC Sciences, USA) was used for sequencing. Basic CASAVA imaging recognition data were converted into a large amount of high-quality raw data. To ensure the accuracy of the subsequent analysis, strict quality control of the read data was carried out using the FASTP software before data analysis. This quality control step includes deleting reads with adapters, pairing high N content reads, and pairing poor-quality reads [47]. After filtering the raw data, checking the sequencing error rate, and checking the distribution of GC content, the remaining clean readings were used for subsequent analyses. For gene annotation, the gene sequences with the National Center for Biotechnology Information non-redundant (NR), Swissprot, Protein families database of alignments and hidden Markov models (Pfam), non-supervised orthologous groups (eggNOG), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were matched using the DIAMOND BLAST software [48]. The amino acid sequence of the gene was matched with the Pfam database using the HMMER software [49]. Use transcripts per million (TPM) to screen genes in transcriptome data and select differentially expressed genes (DEGs) based on p < 0.05 (after calibration) and log₂(Fold Change) ≥ 1 [50]. To elucidate the functions and biological roles of the DEGs, DEGs from the different treatment groups were mapped to terms in the GO database. The number of genes associated with each GO function was calculated. To investigate the metabolic pathways associated with the DEGs and elucidate the biological processes underlying gene interactions, enrichment analysis of DEGs in each KEGG pathway was performed.

Weighted gene co-expression network analysis

Co-expression analysis was performed using the weighted gene correlation network analysis (WGCNA) package in R, following the guidelines of published tutorials [7]. Genes with TPM < 1 in one sample were filtered. Hierarchical clustering of the samples (CK, DL, DM, and DH; each sample was repeated three times, for a total of 12 samples) was conducted based on the Euclidean distance computed from gene expression data and integrated with the physiological indices, plant hormones, functional enzymes, and saikosaponins. Network topology analysis ensured a scale-free topology network with a defined soft thresholding power of 1 [51, 52]. A total of seven modules were identified based on the dynamic tree cutting algorithm with minModuleSize set at 30 and mergeCutHeight set at 0.25 [53]. For each module, the eigengene (the first component expression of genes in the module) was determined, and the correlations of the eigengenes with physiological indices, plant hormones, functional enzymes, and saikosaponins were calculated [54]. Genes with high connectivity in their respective modules were considered hub genes [55]. Co-expression relationships in the modules were analysed and visualised using Gephi 0.10.1.

Determination of plant hormones in B. chinense roots by LC-MS/MS

Standard solution preparation: Stock solutions of individual plant hormones were mixed and prepared in a phytohormone-free matrix to obtain a series of plant hormone calibrators. Certain concentrations of 3-hydroxybutyric acid, indole-3-carboxylic acid, indole-3-carboxaldehyde, isopentenyl adenosine, N-jasmonic acid isoleucine, 1-aminocyclopropanecarboxylic acid, dihydrojasmonic acid, jasmonic acid, salicylic acid, and abscisic acid were used as internal standards (IS). The stock solution and working solution were stored in a refrigerator at − 20 °C.

Metabolite extraction: CK, DL, DM, and DH samples treated for 4 days were resuspended in liquid nitrogen and then added to water by vortexing as the diluted sample. Then, 100 µL of each sample was homogenised with 400 µL of acetonitrile (50%) containing mixed IS and extracted at 4 °C. The mixture was centrifuged at 12,000 rpm for 10 min. The supernatant (300 µL) passed through the HLB sorbent (first flow-through fraction) and was then eluted with 500 µL of acetonitrile (30%) (second flow-through fraction). The two fractions were combined in the same centrifuge tube and mixed thoroughly. Six biological replicates were analysed for each sample. Finally, the solutions were injected into an LC-MS/MS system for analysis.

LC-MS Method: Quantification of plant hormones was conducted at Novogene Co., Ltd. (Beijing, China) using the Ultra-HPLC Mass Spectrometry (UHPLC-MS/MS) System (ExionLC™ AD UHPLC-QTRAP 6500+, AB Sciex, USA). Separation was performed on a Waters Xselect HSS T3 column (2.1 × 150 mm, 2.5 μm) at 45 °C. The mobile phase consisted of an aqueous 0.01% formic acid solution (solvent A) and a 0.01% formic acid solution in acetonitrile (solvent B) at a flow rate of 0.30 mL/min. The solvent gradient was set as follows: initial 1 min, 10% B; 3 min, 10 ~ 50% B; 4 min, 50 ~ 65% B; 6 min, 65 ~ 70% B; 7 min, 70 ~ 100% B; 9.1 min, 100 ~ 10% B; 12 min, 10% B. The mass spectrometer operated in multiple reaction modes. The parameters were as follows: ion sputter voltage (negative mode: − 4,500 V, positive mode: 4,500 V), curtain gas (35 psi), ion source temperature (550 °C), and ion source gas for 1 and 2 (60 psi).

Validation by quantitative real time PCR

Among the hub genes, the AACT2, DXS3, IDI1, SQE1, P450s, and UGTs genes involved in saikosaponin biosynthesis were selected. In quantitative real time PCR (qRT-PCR), gene-specific primers were used to validate the expression of eight saikosaponin biosynthesis pathway genes (EF-1α as the reference gene) (Supplementary Tab. S1). The cDNA of the CK, DL, DM, and DH samples was the same as those used for transcriptome sequencing. The qRT-PCR reaction was conducted with a PowerTrack™ SYBR Green Master Mix kit (Thermo Fisher Scientific, USA) and QuantStudio5 Real-Time PCR system (Thermo Fisher Scientific, USA). The relative expression values were computed with the 2^–ΔΔCt method for each gene, and all reactions were carried out in biological triplicate [56].

Statistical analysis

SPSS software (version 21.0; SPSS Inc., USA) was used to process and statistically analyse the experimental data. GraphPad Prism software (version 9.5; GraphPad Software Inc., San Diego, CA, USA) was used to graphically present the statistically analysed data. WGCNA was performed using OmicStudio tools at https://www.omicstudio.cn/tool.

Supplementary Information

Supplementary Material 1^{(23.6MB, rar)}

Acknowledgements

Not applicable.

Supplementary Information

The following supporting information can be downloaded at: http://bmcplantbiol.biomedcentral.com.

Abbreviations

eggNOG: Evolutionary genealogy of genes: Non-supervised Orthologous Groups
NR: National Center for Biotechnology Information non-redundant
GO: Gene Ontology
Pfam: Protein families database of alignments and hidden Markov models
KEGG: Kyoto Encyclopedia of Genes and Genomes
DEGs: Differential expressed genes
WGCNA: Weighted gene co-expression network analysis
UGT UDP: Glucuronosyltransferase
bHLH: Basic/helix-loop-helix transcription factors
ERF: Ethylene-responsive factors
HD-ZIP: Homeodomain-leucine zipper transcription factors
MYB: Transcription factors
NAC: NAC domain transcription factors
WRKY: WRKY transcription factors

Author contributions

L.Y., X. G., C. D., and W. F. conceived and designed this study. L.Y., X. G., and S. Z. performed the majority of the experiments. L.Y. and X. G. analysed the data and completed the first draft. S. Z., J. W., L. Q., N. D. and L. C. worked with L.Y. to conduct experiments. J. W., L. Q., N. D. and L. C. participated in data analysis and manuscript modification.

Funding

This research was supported by the National Natural Science Foundation of China (grant numbers 82104329 and 32401226), the Key Research and Development Program of Henan Province (grant numbers 251111310500, 231111312700 and 241111310200), the Henan Youth Student Science Foundation Project (grant number 252300423976), the China Postdoctoral Science Foundation (grant number 2024T170252), the International Science and Technology Cooperation Projects in Henan Province (grant number 252102520068), the Postdoctoral Research Foundation of Henan Province (grant number HN2025075) and the Scientific research nursery project of Henan University of Chinese Medicine (grant number MP2024-56).

Data availability

The datasets presented in this study can be found in online repositories (National Center for Biotechnology Information) under accession number PRJNA1136685: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1136685, accessed on 17 July 2024.

Declarations

Ethics approval and consent to participate

All local, national or international guidelines and legislation were adhered to in the production of this study. All Bupleurum chinense samples in this study were collected from cultivated plants on privately owned land in Huang Village, He Village Township, Song County, Luoyang City, Henan Province (34°9′ N, 111°59′ E). Permission was obtained from the landowner prior to collection, and no wild populations were involved.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Linlin Yang and Xupeng Gu contributed equally to this work.

References

1.Zeng C, Zhao J, Chen H, Xin C, Wang B, Yu M, Wei J. Traditional use, germplasm identification, phytochemistry, Pharmacology of Bupleuri Radix: a review. Med Plant Biology. 2023;2:18.
2.Feng L, Chen H, Zhao J, Liu D, Wei Z, Li Y, et al. Auxin induces lateral root formation in<i />Bupleurum<i />: a heme oxygenase dependent approach. Chin Herb Med. 2023;15(1):57–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Yu M, Chen H, Liu Q, Huang J, Semagn K, Liu D, et al. Analysis of unigenes involved in lateral root development in Bupleurum Chinense and B. scorzonerifolium. Planta. 2021;253(6):128. [DOI] [PubMed] [Google Scholar]
4.Jiang H, Yang L, Hou A, Zhang J, Wang S, Man W, Zheng S, Yu H, Wang X, Yang B, et al. Botany, traditional uses, phytochemistry, analytical methods, processing, pharmacology and pharmacokinetics of Bupleuri Radix: a systematic review. Biomed Pharmacother. 2020;131: 110679. [DOI] [PubMed]
5.Wang S-R, Fang W-S. Pentacyclic triterpenoids and their saponins with apoptosis-inducing activity. Curr Top Med Chem. 2009;9(16):1581–96. [DOI] [PubMed] [Google Scholar]
6.Szakiel A, Pączkowski C, Henry M. Influence of environmental abiotic factors on the content of saponins in plants. Phytochem Rev. 2011;10(4):471–91. [Google Scholar]
7.Huang L, Guo L. Secondary metabolites accumulatng and geoherbs fomation under enviromental stress (in Chinses). China J Chin Mater Med. 2007;32(4):277–80. [PubMed] [Google Scholar]
8.Yu F, Wang Q, Wei S, Wang D, Fang Y, Liu F, et al. Effect of genotype and environment on five bioactive components of cultivated licorice (Glycyrrhiza uralensis) populations in Northern China. Biol Pharm Bull. 2015;38(1):75–81. [DOI] [PubMed] [Google Scholar]
9.Yang L, Wen K-S, Ruan X, Zhao Y-X, Wei F, Wang Q. Response of plant secondary metabolites to environmental factors. Molecules. 2018;23(4):762. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Yang L. Theory and technology frontiers of ecological planting of Chinese medicinal materials (in Chinese). J Jilin Agricultural Univ. 2020;42(4):355–63. [Google Scholar]
11.Tan L-L, Cai X, Hu Z-H, Ni X-L. Localization and dynamic change of Saikosaponin in root of Bupleurum Chinense. J Integr Plant Biol. 2008;50(8):951–7. [DOI] [PubMed] [Google Scholar]
12.Hawkins BA, Field R, Cornell HV, Currie DJ, Guégan J-F, Kaufman DM, Kerr JT, Mittelbach GG, Oberdorff T, O’Brien EM, et al. Energy, water, and broad-scale geographic patterns of species richness. Ecology. 2003;84(12):3105–17. [Google Scholar]
13.Gavrilescu M. Water, soil, and plants interactions in a threatened environment. Water. 2021;13(19):2746. [Google Scholar]
14.Yang L, Zhao Y, Zhang Q, Cheng L, Han M, Ren Y, et al. Effects of drought-re-watering-drought on the photosynthesis physiology and secondary metabolite production of Bupleurum Chinense DC. Plant Cell Rep. 2019;38(9):1181–97. [DOI] [PubMed] [Google Scholar]
15.Yang L-L, Yang L, Yang X, Zhang T, Lan Y-M, Zhao Y, et al. Drought stress induces biosynthesis of flavonoids in leaves and saikosaponins in roots of Bupleurum chinense DC. Phytochemistry. 2020;177:112434. 10.1016/j.phytochem.2020.112434. [DOI] [PubMed] [Google Scholar]
16.Yang L-l, Yang L, Lan Y-m, Zhao Y, Han M, Yang L-m. Exogenous abscisic acid reduces saikosaponin accumulation by inhibiting saikosaponin synthesis pathway gene expression under drought stress in Bupleurum chinense DC. Ind Crops Prod. 2020;154:112686. 10.1016/j.indcrop.2020.112686.33162677 [Google Scholar]
17.Yang L, Qiao L, Su X, Ji B, Dong C. Drought stress stimulates the terpenoid backbone and triterpenoid biosynthesis pathway to promote the synthesis of saikosaponin in Bupleurum chinense DC. Roots. Molecules. 2022;27(17):5470. 10.3390/molecules27175470. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Lamalakshmi Devi E, Kumar S, Basanta Singh T, Sharma SK, Beemrote A, Devi CP, Chongtham SK, Singh CH, Yumlembam RA, Haribhushan A et al. Adaptation strategies and defence mechanisms of plants during environmental stress. In: Medicinal Plants and Environmental Challenges. Edited by Ghorbanpour M, Varma A. Cham: Springer International Publishing; 2017: 359–413.
19.Chaudhry S, Sidhu GPS. Climate change regulated abiotic stress mechanisms in plants: a comprehensive review. Plant Cell Rep. 2022;41(1):1–31. [DOI] [PubMed] [Google Scholar]
20.Pandey P, Ramegowda V, Senthil-Kumar M. Shared and unique responses of plants to multiple individual stresses and stress combinations: physiological and molecular mechanisms. Front Plant Sci. 2015;6:723. [DOI] [PMC free article] [PubMed]
21.Cvikrová M, Gemperlová L, Martincová O. Vanková RJPp, biochemistry: effect of drought and combined drought and heat stress on polyamine metabolism in proline-over-producing tobacco plants. Plant Physiol Biochem. 2013;73:7–15. [DOI] [PubMed] [Google Scholar]
22.Casaretto JA, El-Kereamy A, Zeng B, Stiegelmeyer SM, Chen X, Bi Y-M. Rothstein sjjbg: expression of OsMYB55 in maize activates stress-responsive genes and enhances heat and drought tolerance. BMC Genomics. 2016;17:1–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Vashisth D, Kumar R, Rastogi S, Patel VK, Kalra A, Gupta MM, et al. Transcriptome changes induced by abiotic stresses in Artemisia annua. Sci Rep. 2018;8(1):3423. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Waadt R, Seller CA, Hsu P-K, Takahashi Y, Munemasa S, Schroeder JI. Plant hormone regulation of abiotic stress responses. Nat Rev Mol Cell Biol. 2022;23(10):680–94. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Vom Endt D, Kijne JW, Memelink J. Transcription factors controlling plant secondary metabolism: what regulates the regulators? Phytochemistry. 2002;61(2):107–14. [DOI] [PubMed] [Google Scholar]
26.Rowe N, Speck T. Plant growth forms: an ecological and evolutionary perspective. New Phytol. 2005;166(1):61–72. [DOI] [PubMed] [Google Scholar]
27.Lachowiec J, Queitsch C, Kliebenstein DJ. Molecular mechanisms governing differential robustness of development and environmental responses in plants. Ann Bot. 2015;117(5):795–809. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Suzuki N, Rivero RM, Shulaev V, Blumwald E, Mittler R. Abiotic and biotic stress combinations. New Phytol. 2014;203(1):32–43. [DOI] [PubMed] [Google Scholar]
29.Cramer GR, Urano K, Delrot S, Pezzotti M, Shinozaki K. Effects of abiotic stress on plants: a systems biology perspective. BMC Plant Biol. 2011;11(1):163. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Ramegowda V, Senthil-Kumar M. The interactive effects of simultaneous biotic and abiotic stresses on plants: mechanistic understanding from drought and pathogen combination. J Plant Physiol. 2015;176:47–54. [DOI] [PubMed] [Google Scholar]
31.Huang H, Han Y, Hao J, Qin X, Liu C, Fan S. Exogenous spermidine modulates osmoregulatory substances and leaf stomata to alleviate the damage to lettuce seedlings caused by high temperature stress. J Plant Growth Regul. 2023;42(2):1236–55. [Google Scholar]
32.Yamauchi Y, Furutera A, Seki K, Toyoda Y, Tanaka K, Sugimoto Y. Malondialdehyde generated from peroxidized linolenic acid causes protein modification in heat-stressed plants. Plant Physiol Biochem. 2008;46(8):786–93. [DOI] [PubMed] [Google Scholar]
33.Liao Z, Fan J, Lai Z, Bai Z, Wang H, Cheng M, Zhang F, Li Z. Chapter Three - Response network and regulatory measures of plant-soil-rhizosphere environment to drought stress. Advances in Agronomy. 2023;180:93–196.
34.Li Y, Kong D, Fu Y, Sussman MR, Wu H. The effect of developmental and environmental factors on secondary metabolites in medicinal plants. Plant Physiol Biochem. 2020;148:80–9. [DOI] [PubMed] [Google Scholar]
35.Alami MM, Guo S, Mei Z, Yang G, Wang X. Environmental factors on secondary metabolism in medicinal plants: exploring accelerating factors. Med Plant Biol. 2024;3:e016. 10.48130/mpb-0024-0016. [Google Scholar]
36.Lei H, Zhang H, Zhang Z, Sun H, Li M, Shao C, Liang H, Wu H, Zhang Y. Physiological and transcriptomic analyses of roots from Panax ginseng C. A. Meyer under drought stress. Ind Crops Prod. 2023;191:115858. [Google Scholar]
37.Zhang T, Gao Y, Han M, Yang L. Changes in the physiological characteristics of Panax ginseng embryogenic calli and molecular mechanism of ginsenoside biosynthesis under cold stress. Planta. 2021;253(4):79. [DOI] [PubMed] [Google Scholar]
38.Han Y, Hou Z, Zhang X, Yan K, Liang Z, He Q. Important changes in germination, seedling tolerance, and active components content due to drought stress on three licorice (Glycyrrhiza) species. Ind Crops Prod. 2022;175:114240. [Google Scholar]
39.Bari R, Jones JDG. Role of plant hormones in plant defence responses. Plant Mol Biol. 2009;69(4):473–88. [DOI] [PubMed] [Google Scholar]
40.Zaikina EA, Rumyantsev SD, Sarvarova ER, Kuluev BR. Transcription factor genes involved in plant response to abiotic stress factors. EcoGen. 2019;17(3):47–58. [Google Scholar]
41.Yoon Y, Seo DH, Shin H, Kim HJ, Kim CM, Jang G. The role of stress-responsive transcription factors in modulating abiotic stress tolerance in plants. Agronomy. 2020;10(6):788. [Google Scholar]
42.Ambawat S, Sharma P, Yadav NR, Yadav RC. MYB transcription factor genes as regulators for plant responses: an overview. Physiol Mol Biol Plants. 2013;19(3):307–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Zhou C, Zhu C, Xie S, Weng J, Lin Y, Lai Z, Guo Y. Genome-wide analysis of zinc finger motif-associated homeodomain (ZF-HD) family genes and their expression profiles under abiotic stresses and phytohormones stimuli in tea plants (Camellia sinensis). Sci Hort. 2021;281:109976. [Google Scholar]
44.Wani SH, Anand S, Singh B, Bohra A, Joshi R. WRKY transcription factors and plant defense responses: latest discoveries and future prospects. Plant Cell Rep. 2021;40(7):1071–85. [DOI] [PubMed] [Google Scholar]
45.Kelimujiang K, Zhang W, Zhang X, Waili A, Tang X, Chen Y, et al. Genome-wide investigation of WRKY gene family in Lavandula angustifolia and potential role of LaWRKY57 and LaWRKY75 in the regulation of terpenoid biosynthesis. Front Plant Sci. 2024;15:1449299. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Han J, Li T, Wang X, Zhang X, Bai X, Shao H, Wang S, Hu Z, Wu J, Leng P. AmMYB24 regulates floral terpenoid biosynthesis induced by blue light in snapdragon flowers. Front Plant Sci. 2022;13:885168. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal. 2011;17(1):10–2. [Google Scholar]
48.Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12(1):59–60. [DOI] [PubMed] [Google Scholar]
49.Finn RD, Clements J, Eddy SR. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 2011;39(suppl2):W29–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5(7):621–8. [DOI] [PubMed] [Google Scholar]
51.Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9(1):559. [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Li X, Zheng Y, Liu M, Wang K, Chen H. Weighted gene co-expression network analysis and identification of ginsenoside biosynthesis candidate genes for ginseng adventitious roots under MeJA treatment. Genes Genomics. 2024;46(12):1473–85. [DOI] [PubMed] [Google Scholar]
53.Chen J, Wang J, Liu L, Pei Y, Liu Z, Feng X, et al. Transcriptomic and metabolomic profiling provide insight into the role of sugars and hormones in leaf senescence of Pinellia Ternata. Plant Cell Rep. 2024;43(5):125. [DOI] [PubMed] [Google Scholar]
54.Liu F, Zhao Y, Wang X, Wang B, Xiao F, He K. Transcriptome analysis reveals regulatory mechanisms of different drought-tolerant Gleditsia sinensis seedlings under drought stress. BMC Genomic Data. 2024;25(1):29. [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Xie N, Guo Q, Li H, Yuan G, Gui Q, Xiao Y, et al. Integrated transcriptomic and WGCNA analyses reveal candidate genes regulating mainly flavonoid biosynthesis in Litsea Coreana var. sinensis. BMC Plant Biol. 2024;24(1):231. [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2^–∆∆CT method. Methods. 2001;25(4):402–8. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1^{(23.6MB, rar)}

Data Availability Statement

[CR1] 1.Zeng C, Zhao J, Chen H, Xin C, Wang B, Yu M, Wei J. Traditional use, germplasm identification, phytochemistry, Pharmacology of Bupleuri Radix: a review. Med Plant Biology. 2023;2:18.

[CR2] 2.Feng L, Chen H, Zhao J, Liu D, Wei Z, Li Y, et al. Auxin induces lateral root formation in<i />Bupleurum<i />: a heme oxygenase dependent approach. Chin Herb Med. 2023;15(1):57–62. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR3] 3.Yu M, Chen H, Liu Q, Huang J, Semagn K, Liu D, et al. Analysis of unigenes involved in lateral root development in Bupleurum Chinense and B. scorzonerifolium. Planta. 2021;253(6):128. [DOI] [PubMed] [Google Scholar]

[CR4] 4.Jiang H, Yang L, Hou A, Zhang J, Wang S, Man W, Zheng S, Yu H, Wang X, Yang B, et al. Botany, traditional uses, phytochemistry, analytical methods, processing, pharmacology and pharmacokinetics of Bupleuri Radix: a systematic review. Biomed Pharmacother. 2020;131: 110679. [DOI] [PubMed]

[CR5] 5.Wang S-R, Fang W-S. Pentacyclic triterpenoids and their saponins with apoptosis-inducing activity. Curr Top Med Chem. 2009;9(16):1581–96. [DOI] [PubMed] [Google Scholar]

[CR6] 6.Szakiel A, Pączkowski C, Henry M. Influence of environmental abiotic factors on the content of saponins in plants. Phytochem Rev. 2011;10(4):471–91. [Google Scholar]

[CR7] 7.Huang L, Guo L. Secondary metabolites accumulatng and geoherbs fomation under enviromental stress (in Chinses). China J Chin Mater Med. 2007;32(4):277–80. [PubMed] [Google Scholar]

[CR8] 8.Yu F, Wang Q, Wei S, Wang D, Fang Y, Liu F, et al. Effect of genotype and environment on five bioactive components of cultivated licorice (Glycyrrhiza uralensis) populations in Northern China. Biol Pharm Bull. 2015;38(1):75–81. [DOI] [PubMed] [Google Scholar]

[CR9] 9.Yang L, Wen K-S, Ruan X, Zhao Y-X, Wei F, Wang Q. Response of plant secondary metabolites to environmental factors. Molecules. 2018;23(4):762. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR10] 10.Yang L. Theory and technology frontiers of ecological planting of Chinese medicinal materials (in Chinese). J Jilin Agricultural Univ. 2020;42(4):355–63. [Google Scholar]

[CR11] 11.Tan L-L, Cai X, Hu Z-H, Ni X-L. Localization and dynamic change of Saikosaponin in root of Bupleurum Chinense. J Integr Plant Biol. 2008;50(8):951–7. [DOI] [PubMed] [Google Scholar]

[CR12] 12.Hawkins BA, Field R, Cornell HV, Currie DJ, Guégan J-F, Kaufman DM, Kerr JT, Mittelbach GG, Oberdorff T, O’Brien EM, et al. Energy, water, and broad-scale geographic patterns of species richness. Ecology. 2003;84(12):3105–17. [Google Scholar]

[CR13] 13.Gavrilescu M. Water, soil, and plants interactions in a threatened environment. Water. 2021;13(19):2746. [Google Scholar]

[CR14] 14.Yang L, Zhao Y, Zhang Q, Cheng L, Han M, Ren Y, et al. Effects of drought-re-watering-drought on the photosynthesis physiology and secondary metabolite production of Bupleurum Chinense DC. Plant Cell Rep. 2019;38(9):1181–97. [DOI] [PubMed] [Google Scholar]

[CR15] 15.Yang L-L, Yang L, Yang X, Zhang T, Lan Y-M, Zhao Y, et al. Drought stress induces biosynthesis of flavonoids in leaves and saikosaponins in roots of Bupleurum chinense DC. Phytochemistry. 2020;177:112434. 10.1016/j.phytochem.2020.112434. [DOI] [PubMed] [Google Scholar]

[CR16] 16.Yang L-l, Yang L, Lan Y-m, Zhao Y, Han M, Yang L-m. Exogenous abscisic acid reduces saikosaponin accumulation by inhibiting saikosaponin synthesis pathway gene expression under drought stress in Bupleurum chinense DC. Ind Crops Prod. 2020;154:112686. 10.1016/j.indcrop.2020.112686.33162677 [Google Scholar]

[CR17] 17.Yang L, Qiao L, Su X, Ji B, Dong C. Drought stress stimulates the terpenoid backbone and triterpenoid biosynthesis pathway to promote the synthesis of saikosaponin in Bupleurum chinense DC. Roots. Molecules. 2022;27(17):5470. 10.3390/molecules27175470. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR18] 18.Lamalakshmi Devi E, Kumar S, Basanta Singh T, Sharma SK, Beemrote A, Devi CP, Chongtham SK, Singh CH, Yumlembam RA, Haribhushan A et al. Adaptation strategies and defence mechanisms of plants during environmental stress. In: Medicinal Plants and Environmental Challenges. Edited by Ghorbanpour M, Varma A. Cham: Springer International Publishing; 2017: 359–413.

[CR19] 19.Chaudhry S, Sidhu GPS. Climate change regulated abiotic stress mechanisms in plants: a comprehensive review. Plant Cell Rep. 2022;41(1):1–31. [DOI] [PubMed] [Google Scholar]

[CR20] 20.Pandey P, Ramegowda V, Senthil-Kumar M. Shared and unique responses of plants to multiple individual stresses and stress combinations: physiological and molecular mechanisms. Front Plant Sci. 2015;6:723. [DOI] [PMC free article] [PubMed]

[CR21] 21.Cvikrová M, Gemperlová L, Martincová O. Vanková RJPp, biochemistry: effect of drought and combined drought and heat stress on polyamine metabolism in proline-over-producing tobacco plants. Plant Physiol Biochem. 2013;73:7–15. [DOI] [PubMed] [Google Scholar]

[CR22] 22.Casaretto JA, El-Kereamy A, Zeng B, Stiegelmeyer SM, Chen X, Bi Y-M. Rothstein sjjbg: expression of OsMYB55 in maize activates stress-responsive genes and enhances heat and drought tolerance. BMC Genomics. 2016;17:1–15. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR23] 23.Vashisth D, Kumar R, Rastogi S, Patel VK, Kalra A, Gupta MM, et al. Transcriptome changes induced by abiotic stresses in Artemisia annua. Sci Rep. 2018;8(1):3423. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR24] 24.Waadt R, Seller CA, Hsu P-K, Takahashi Y, Munemasa S, Schroeder JI. Plant hormone regulation of abiotic stress responses. Nat Rev Mol Cell Biol. 2022;23(10):680–94. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR25] 25.Vom Endt D, Kijne JW, Memelink J. Transcription factors controlling plant secondary metabolism: what regulates the regulators? Phytochemistry. 2002;61(2):107–14. [DOI] [PubMed] [Google Scholar]

[CR26] 26.Rowe N, Speck T. Plant growth forms: an ecological and evolutionary perspective. New Phytol. 2005;166(1):61–72. [DOI] [PubMed] [Google Scholar]

[CR27] 27.Lachowiec J, Queitsch C, Kliebenstein DJ. Molecular mechanisms governing differential robustness of development and environmental responses in plants. Ann Bot. 2015;117(5):795–809. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR28] 28.Suzuki N, Rivero RM, Shulaev V, Blumwald E, Mittler R. Abiotic and biotic stress combinations. New Phytol. 2014;203(1):32–43. [DOI] [PubMed] [Google Scholar]

[CR29] 29.Cramer GR, Urano K, Delrot S, Pezzotti M, Shinozaki K. Effects of abiotic stress on plants: a systems biology perspective. BMC Plant Biol. 2011;11(1):163. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR30] 30.Ramegowda V, Senthil-Kumar M. The interactive effects of simultaneous biotic and abiotic stresses on plants: mechanistic understanding from drought and pathogen combination. J Plant Physiol. 2015;176:47–54. [DOI] [PubMed] [Google Scholar]

[CR31] 31.Huang H, Han Y, Hao J, Qin X, Liu C, Fan S. Exogenous spermidine modulates osmoregulatory substances and leaf stomata to alleviate the damage to lettuce seedlings caused by high temperature stress. J Plant Growth Regul. 2023;42(2):1236–55. [Google Scholar]

[CR32] 32.Yamauchi Y, Furutera A, Seki K, Toyoda Y, Tanaka K, Sugimoto Y. Malondialdehyde generated from peroxidized linolenic acid causes protein modification in heat-stressed plants. Plant Physiol Biochem. 2008;46(8):786–93. [DOI] [PubMed] [Google Scholar]

[CR33] 33.Liao Z, Fan J, Lai Z, Bai Z, Wang H, Cheng M, Zhang F, Li Z. Chapter Three - Response network and regulatory measures of plant-soil-rhizosphere environment to drought stress. Advances in Agronomy. 2023;180:93–196.

[CR34] 34.Li Y, Kong D, Fu Y, Sussman MR, Wu H. The effect of developmental and environmental factors on secondary metabolites in medicinal plants. Plant Physiol Biochem. 2020;148:80–9. [DOI] [PubMed] [Google Scholar]

[CR35] 35.Alami MM, Guo S, Mei Z, Yang G, Wang X. Environmental factors on secondary metabolism in medicinal plants: exploring accelerating factors. Med Plant Biol. 2024;3:e016. 10.48130/mpb-0024-0016. [Google Scholar]

[CR36] 36.Lei H, Zhang H, Zhang Z, Sun H, Li M, Shao C, Liang H, Wu H, Zhang Y. Physiological and transcriptomic analyses of roots from Panax ginseng C. A. Meyer under drought stress. Ind Crops Prod. 2023;191:115858. [Google Scholar]

[CR37] 37.Zhang T, Gao Y, Han M, Yang L. Changes in the physiological characteristics of Panax ginseng embryogenic calli and molecular mechanism of ginsenoside biosynthesis under cold stress. Planta. 2021;253(4):79. [DOI] [PubMed] [Google Scholar]

[CR38] 38.Han Y, Hou Z, Zhang X, Yan K, Liang Z, He Q. Important changes in germination, seedling tolerance, and active components content due to drought stress on three licorice (Glycyrrhiza) species. Ind Crops Prod. 2022;175:114240. [Google Scholar]

[CR39] 39.Bari R, Jones JDG. Role of plant hormones in plant defence responses. Plant Mol Biol. 2009;69(4):473–88. [DOI] [PubMed] [Google Scholar]

[CR40] 40.Zaikina EA, Rumyantsev SD, Sarvarova ER, Kuluev BR. Transcription factor genes involved in plant response to abiotic stress factors. EcoGen. 2019;17(3):47–58. [Google Scholar]

[CR41] 41.Yoon Y, Seo DH, Shin H, Kim HJ, Kim CM, Jang G. The role of stress-responsive transcription factors in modulating abiotic stress tolerance in plants. Agronomy. 2020;10(6):788. [Google Scholar]

[CR42] 42.Ambawat S, Sharma P, Yadav NR, Yadav RC. MYB transcription factor genes as regulators for plant responses: an overview. Physiol Mol Biol Plants. 2013;19(3):307–21. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR43] 43.Zhou C, Zhu C, Xie S, Weng J, Lin Y, Lai Z, Guo Y. Genome-wide analysis of zinc finger motif-associated homeodomain (ZF-HD) family genes and their expression profiles under abiotic stresses and phytohormones stimuli in tea plants (Camellia sinensis). Sci Hort. 2021;281:109976. [Google Scholar]

[CR44] 44.Wani SH, Anand S, Singh B, Bohra A, Joshi R. WRKY transcription factors and plant defense responses: latest discoveries and future prospects. Plant Cell Rep. 2021;40(7):1071–85. [DOI] [PubMed] [Google Scholar]

[CR45] 45.Kelimujiang K, Zhang W, Zhang X, Waili A, Tang X, Chen Y, et al. Genome-wide investigation of WRKY gene family in Lavandula angustifolia and potential role of LaWRKY57 and LaWRKY75 in the regulation of terpenoid biosynthesis. Front Plant Sci. 2024;15:1449299. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR46] 46.Han J, Li T, Wang X, Zhang X, Bai X, Shao H, Wang S, Hu Z, Wu J, Leng P. AmMYB24 regulates floral terpenoid biosynthesis induced by blue light in snapdragon flowers. Front Plant Sci. 2022;13:885168. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR47] 47.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal. 2011;17(1):10–2. [Google Scholar]

[CR48] 48.Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12(1):59–60. [DOI] [PubMed] [Google Scholar]

[CR49] 49.Finn RD, Clements J, Eddy SR. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 2011;39(suppl2):W29–37. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR50] 50.Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5(7):621–8. [DOI] [PubMed] [Google Scholar]

[CR51] 51.Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9(1):559. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR52] 52.Li X, Zheng Y, Liu M, Wang K, Chen H. Weighted gene co-expression network analysis and identification of ginsenoside biosynthesis candidate genes for ginseng adventitious roots under MeJA treatment. Genes Genomics. 2024;46(12):1473–85. [DOI] [PubMed] [Google Scholar]

[CR53] 53.Chen J, Wang J, Liu L, Pei Y, Liu Z, Feng X, et al. Transcriptomic and metabolomic profiling provide insight into the role of sugars and hormones in leaf senescence of Pinellia Ternata. Plant Cell Rep. 2024;43(5):125. [DOI] [PubMed] [Google Scholar]

[CR54] 54.Liu F, Zhao Y, Wang X, Wang B, Xiao F, He K. Transcriptome analysis reveals regulatory mechanisms of different drought-tolerant Gleditsia sinensis seedlings under drought stress. BMC Genomic Data. 2024;25(1):29. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR55] 55.Xie N, Guo Q, Li H, Yuan G, Gui Q, Xiao Y, et al. Integrated transcriptomic and WGCNA analyses reveal candidate genes regulating mainly flavonoid biosynthesis in Litsea Coreana var. sinensis. BMC Plant Biol. 2024;24(1):231. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR56] 56.Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2^–∆∆CT method. Methods. 2001;25(4):402–8. [DOI] [PubMed] [Google Scholar]

PERMALINK

Combined drought and high temperature stress enhances saikosaponin biosynthesis in Bupleurum Chinense through multi-level regulatory mechanisms

Linlin Yang

Xupeng Gu

Shengwei Zhou

Jie Wan

Lu Qiao

Ning Dong

Leixia Chu

Chengming Dong

Weisheng Feng

Abstract

Background

Results

Conclusions

Supplementary Information

Introduction

Fig. 1.

Results

The effect of two-factor (drought and temperature) combined stress on the plant height and root length of B. chinense

Fig. 2.

The effect of two-factor (drought and temperature) combined stress on the physiological indices of B. chinense

Fig. 3.

The effect of two-factor (drought and temperature) combined stress on the key enzyme activities in the terpenoid synthesis pathway

Fig. 4.

The effect of two-factor (drought and temperature) combined stress on the saikosaponins content in B. chinense roots

Fig. 5.

Transcript abundance analysis of DEGs of B. chinense roots treated with two-factor (drought and temperature) combined stress

Fig. 6.

Fig. 7.

Combined stress response-related modules obtained by weighted co‑expression network analysis

DEGs involved in the saikosaponins and abscisic acid biosynthesis under combined stress

Fig. 8.

Analysis of DEGs of transcription factors related to saikosaponins and abscisic acid biosynthesis under combined stress

Fig. 9.

Validation by qRT-PCR

Fig. 10.

Discussion

The impact of environmental stress on the growth and physiological changes of B. chinense

Medicinal plants cope with environmental stress by synthesising secondary metabolites

Possible mechanism of the “short-term promotional effect” of two-factor combined stress on the synthesis of saikosaponins in B. chinense

Conclusion

Materials and methods

Experimental materials and treatment methods

Fig. 11.

Determination of physiological indices

Determination of key enzyme activities in the terpenoid synthesis pathway

Determination of saikosaponin content in B. chinense roots

Transcriptome sequencing and data analysis

Weighted gene co-expression network analysis

Determination of plant hormones in B. chinense roots by LC-MS/MS

Validation by quantitative real time PCR

Statistical analysis

Supplementary Information

Acknowledgements

Supplementary Information

Abbreviations

Author contributions

Funding

Data availability

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Footnotes

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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