Structural characterisation and discrimination of the aerial parts of Paris polyphylla var. yunnanensis and Paris polyphylla var. chinensis by UHPLC-QTOF-MS coupled with multivariate data analysis

Promotion of a quality standard for Paris polyphylla var. yunnanensis based on the efficacy-oriented effect-constituent index

Multi-component determination and bioassays used for the quality control of traditional Chinese medicine (TCM) may have certain shortcomings. The effect-constituent index (ECI) is a quality evaluation index weighted by chemical composition analysis and effect detection. This index can be established by the dose-effect relationship of the active ingredients in TCM. In this study, Paris polyphylla var. yunnanensis (PY) was selected as the representative drug. Chemical evaluation and bioactivity evaluation were combined to establish the ECI, to compensate for the deficiency of a single evaluation method to some extent, and can be related to the efficacy of PY, in order to improve its quality standard. The ECI not only reflects the contribution of component content to the quality of PY, but also relates to clinical efficacy and reflects the influence of different components on the biological activity. Moreover, this study provided a reference method for the quality control of other TCMs.

Polyphyllin II inhibits NLPR3 inflammasome activation and inflammatory response of Mycobacterium tuberculosis-infected human bronchial epithelial cells

BACKGROUND

The bronchial infection by Mycobacterium tuberculosis (Mtb) is increasing in prevalence and severity worldwide. Despite appropriate tuberculosis treatment, most patients still develop bronchial stenosis, which often leads to disability. Polyphyllin II (PP2) is a steroidal saponin extracted from Rhizoma Paridis. In this study, we aimed to explore the effect of PP2 on the advancement of Mtb-induced bronchial infection.

METHOD

The effects of PP2 on cell viability were measured by using MTT and lactate dehydrogenase (LDH) kit. The mRNA and protein levels of tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-8 were elucidated by RT-qPCR and ELISA, respectively. The expression of NLR family pyrin domain containing 3 (NLRP3) related inflammasome (NLRP3, IL-1β, and cleaved-caspase-1) and the activated degree of protein kinase B (AKT)/nuclear factor-kappa B (NF-kB; p-AKT and p-NF-κB) were detected by Western blotting.

RESULTS

PP2 at 0, 1, 5, and 10 μM had little cytotoxicity on 16HBE cells. PP2 inhibited Mtb-induced cell proliferation and decreased LDH levels. We further found that PP2 could suppress Mtb-induced inflammatory responses and activation of NLPR3 inflammasome. Additionally, the role of PP2 in Mtb is associated with the AKT/NF-kB signaling pathway.

CONCLUSION

PP2 inhibited Mtb infection in bronchial epithelial cells, by inhibiting Mtb-induced inflammatory reactions and activation of NLPR3 inflammasome. These effects may be exerted by suppressing the AKT/NF-kB pathway, which will provide a prospective treatment.

Separation and Purification of Two Saponins from Paris polyphylla var. yunnanensis by a Macroporous Resin

An effective method for separating and purifying critical saponins (polyphyllin II and polyphyllin VII) from a Paris polyphylla var. yunnanensis extract was developed in this study which was environmentally friendly and economical. Static adsorption kinetics, thermodynamics, and the dynamic adsorption-desorption of macroporous resins were investigated, and then the conditions of purification and separation were optimized by fitting with an adsorption thermodynamics equation and a kinetic equation. Effective NKA-9 resin from seven macroporous resins was screened out to separate and purify the two saponins. The static adsorption and dynamic adsorption were chemical and physical adsorption dual-processes on the NKA-9 resin. Under the optimum parameters, the contents of polyphyllin II and polyphyllin VII in the product were 17.3-fold and 28.6-fold those in plant extracts, respectively. The total yields of the two saponins were 93.16%. This research thus provides a theoretical foundation for the large-scale industrial production of the natural drugs polyphyllin II and polyphyllin VII.

A fast multi-source information fusion strategy based on deep learning for species identification of boletes

Wild mushroom market is an important economic source of Yunnan province in China, and its wild mushroom resources are also valuable wealth in the world. This work will put forward a method of species identification and optimize the method in order to maintain the market order and protect the economic benefits of wild mushrooms. Here we establish deep learning (DL) models based on the two-dimensional correlation spectroscopy (2DCOS) images of near-infrared spectroscopy from boletes, and optimize the identification effect of the model. The results show that synchronous 2DCOS is the best method to establish DL model, and when the learning rate was 0.01, the epochs were 40, using stipes and caps data, the identification effect would be further improved. This method retains the complete information of the samples and can provide a fast and noninvasive method for identifying boletes species for market regulators.

Study of Chemical Compositions and Anticancer Effects of Paris polyphylla var. Chinensis Leaves

Paris polyphylla var. chinensis (Franch.) Hara is a perennial herb belonging to the Trilliaceae family. Ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC/Q–TOF MS) was used to detect the composition of different fractions of Paris polyphylla var. chinensis leaves. Meanwhile, the extracts of different fractions were evaluated for their cytotoxic activities against four selected human cancer cell lines and one human normal epithelial cell line based on the MTT assay method. Multivariate statistical analysis was performed to screen differential compounds and to analyze the distributions between different fractions. Finally, more than 60 compounds were obtained and identified from the different fractions of Paris polyphylla var. chinensis leaves, and the chloroform and n−butanol extracts showed significant cytotoxic effects on these four cancer cells. Several compounds were preliminarily identified from different fractions, including 36 steroidal saponins, 11 flavonoids, 10 ceramides, 8 lipids, 6 organic acids, and 8 other compounds. Various compounds were screened out as different chemical components of different fractions, which were considered as a potential substance basis for the cytotoxicity of Paris polyphylla var. chinensis leaves.

Extended application of deep learning combined with 2DCOS: Study on origin identification in the medicinal plant of Paris polyphylla var. yunnanensis

INTRODUCTION

Medicinal plants are very important to human health, and ensuring their quality and rapid evaluation are the current research concerns. Deep learning has a strong ability in recognition. This study extended it to the identification of medicinal plants from the perspective of spectrum.

OBJECTIVE

In order to realise the rapid identification and provide a reference for the selection of high-quality resources of medicinal plants, a combination of deep learning and two-dimensional correlation spectroscopy (2DCOS) was proposed.

METHODS

For the first time, Fourier transform mid-infrared (FT-MIR) and near-infrared (NIR) spectroscopy 2DCOS images combined with residual neural network (ResNet) was used for the origin identification of Paris polyphylla var. yunnanensis. In total 1593 samples were collected and 12821 2DCOS images were drawn. The climate of different origins was briefly analysed.

RESULTS

The xishuangbanna, puer, lincang, honghe and wenshan are the five regions with more ecological advantages. The synchronous 2DCOS models of FT-MIR and NIR could realise origin identification with the accuracy of 100%. The synchronous images were suitable for the identification of medicinal plants with complex systems. The full band, feature band and different contour models had no big difference in distinguishing ability, so they were not the key factors affecting the discrimination results.

CONCLUSION

The ResNet models established were stable, reliable, and robust, which not only solved the problem of origin identification, expanded the application field of deep learning, but also provided practical reference for the related research of other medicinal plants.

Study on the identification and evaluation of growth years for Paris polyphylla var. yunnanensis using deep learning combined with 2DCOS

Paris polyphylla var. yunnanensis, as perennial plants, its quality is closely related to growth period. Different harvest years determine the dry matter accumulation of its medicinal parts and the dynamic accumulation of active ingredients, as well as its economic value and medicinal value. Therefore, it is necessary to establish a systematic evaluation method for the identification and evaluation of P. polyphylla var. yunnanensis with different growth years. Deep learning has a powerful ability in recognition. This study extends it to the identification analysis of medicinal plants from the perspective of spectrum. For the first time, two-dimensional correlation spectroscopy (2DCOS) based on the attenuated total reflection Fourier transformed infrared spectroscopy (ATR-FTIR) combined with residual neural network (Resnet) was used to identify growth years. 525 samples were collected, 4725 2DCOS images were drawn, and the dry matter accumulation in rhizomes of different growth years and different sampling sites were briefly analyzed. The results show that the eight-year-old P. polyphylla var. yunnanensis in Dali has higher economic value and medicinal value. The synchronous 2DCOS models based on ATR-FTIR can realize the identification of growth years with accuracy of 100%. Synchronous 2DCOS are more suitable for the identification of medicinal plants with complex systems. 2DCOS images with different colors and second derivative processing cannot optimize the modeling results. In summary, the method we established is innovative and feasible. It not only solved the identification of growth years, expanded the application field of deep learning, but could also be extended to further research on other medicinal plants.

The Pharmacokinetics and Tissue Distributions of Nine Steroidal Saponins from Paris polyphylla in Rats

BACKGROUND AND OBJECTIVES

Paris polyphylla (P. polyphylla) is a herb widely used in traditional Chinese medicine to treat various diseases. This study used ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) to study the pharmacokinetics and tissue distributions of nine steroidal saponins from P. polyphylla.

METHODS

P. polyphylla extract was administered to rats intravenously (i.v.) and orally (p.o.). The concentrations of the nine main bioactive components of the extract were determined in plasma and tissue samples using UPLC-MS/MS. The nine saponin compounds were also incubated in an anaerobic environment with intestinal flora suspension solution to investigate hydrolysis by intestinal flora.

RESULTS

After oral administration of the P. polyphylla extract, polyphyllin VII was found to have the highest maximum concentration (C_max, 17.0 ± 2.24 µg/L) of all nine components, followed by the C_max values of dioscin (16.17 ± 0.64 µg/L) and polyphyllin H (11.75 ± 1.28 µg/L), while the C_max values of polyphyllin I, polyphyllin II, progenin III, polyphyllin IV, gracillin, and polyphyllin were less than 10 µg/L. The bioavailabilities of all nine components were less than 1%. All the compounds were hydrolyzed by intestinal flora and were predominantly distributed in the liver and lungs.

CONCLUSIONS

The nine compounds presented different pharmacokinetic parameter values, and multiple administrations did not accumulate in the body. The bioavailabilities of the compounds were low, partly because of hydrolysis by intestinal flora. The nine compounds were mainly distributed in the liver and lungs, which may be target organs.

Molecular-networking-guided discovery of species-specific markers for discriminating five medicinal Paris herbs

BACKGROUND

Paridis Rhizoma (PR) is a famous traditional herbal medicine. Apart from two officially recorded species, viz. Paris polyphylla Smith var. yunnanensis (Franch.) Hand. - Mazz. (PPY) and P. polyphylla Smith var. chinensis (Franch.) Hara (PPC), there are still many other species used as folk medicine. It is necessary to understand the metabolic differences among Paris species.

PURPOSE

To establish a strategy that can discover species-specific steroidal saponin markers to distinguish closely-related Paris herbs for quality and safety control.

METHODS

A new strategy of molecular-networking-guided discovery of species-specific markers was proposed. Firstly, the ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was applied to obtain the MS and MS/MS data of all samples. Then, molecular networking (MN) was created using MS/MS data to prescreen the steroidal saponins for subsequent analysis. Next, the principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA) models were established to discover potential markers. Finally, the verification, identification and distribution of chemical markers were performed.

RESULTS

A total of 126 steroidal saponins were screened out from five species using MN. Five species were classified successfully by OPLS-DA model, and 18 species-specific markers were discovered combining the variable importance in the projection (VIP) value, P value (one-way ANOVA) and their relative abundance. These markers could predict the species of Paris herbs correctly.

CONCLUSION

These results revealed that this new strategy could be an efficient way for chemical discrimination of medicinal herbs with close genetic relationship.

Transcriptome analysis of Paris polyphylla var. yunnanensis illuminates the biosynthesis and accumulation of steroidal saponins in rhizomes and leaves

Paris polyphylla var. yunnanensis can synthesize Paris saponins with multiple effective therapies, and its rhizome has become an indispensable ingredient in many patented drugs. However, how Paris saponin content changes in tissues at different stages and the molecular mechanisms underlying the production and accumulation of the bioactive compounds are unclear. This study aimed to uncover the mechanisms underlying the biosynthesis and accumulation by integrating transcriptome sequencing and phytochemical investigation of the leaves and rhizomes at different growth stages. Paris saponin content in leaves was lower during the fruiting stage than the vegetative stage, whereas the content in rhizomes increased during the fruiting stage. The candidate genes related to Paris saponin biosynthesis were determined by transcriptome analyses. Most biosynthetic genes were found to be abundantly expressed in the leaves during the vegetative stage in the light of expression profiles and functional enrichment results. The expression patterns of the differentially expressed genes related to the biosynthesis were positively correlated with the accumulation of saponins in tissues. These findings suggest that both leaves and rhizomes are capable of biosynthesizing Paris saponins, and that aerial plant parts can be used to extract them. The different patterns of biosynthesis and accumulation in the leaves and rhizomes were also determined here. This study will help improve our understanding of the mechanisms underlying the biosynthesis and accumulation of Paris saponins, and aid in the comprehensive development and utilization of this medicinal plant.

Bacterial Diversity in Roots, Stems, and Leaves of Chinese Medicinal Plant Paris polyphylla var. yunnanensis

The root of Paris polyphylla var. yunnanensis, a famous and endangered traditional Chinese herb, has a significant medicinal value. The aim of this study was to analyze the composition and functional characteristics of bacterial endophytes in roots, stems, and leaves of P. polyphylla var. yunnanensis. The 16S rRNA gene sequencing and functional prediction of bacterial endophytes in roots, stems, and leaves of P. polyphylla var. yunnanensis were conducted. The Chao and Shannon indices of the bacteria in roots were significantly higher than those in stems and leaves. The dominant endophyte phyla were Cyanobacteria, Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria. The main genera detected in roots were unclassified Cyanobacteria, Rhizobium, Flavobacterium, and Sphingobium; the main genera in stems were norank_c__Cyanobacteria, Bacillus, and Pseudomonas; the main genera in leaves were norank_c__Cyanobacteria and Rhizobium. The microbiota in roots was particularly enriched in functional categories "extracellular structures" and "cytoskeleton" compared with stems and leaves (p < 0.05). Our study reveals the structural and functional characteristics of the endophytic bacteria in roots, stems, and leaves of P. polyphylla var. yunnanensis, which aids in the scientific understanding of this plant.

The root of Paris polyphylla var. yunnanensis, a famous and endangered traditional Chinese herb, has a significant medicinal value. The aim of this study was to analyze the composition and functional characteristics of bacterial endophytes in roots, stems, and leaves of P. polyphylla var. yunnanensis. The 16S rRNA gene sequencing and functional prediction of bacterial endophytes in roots, stems, and leaves of P. polyphylla var. yunnanensis were conducted. The Chao and Shannon indices of the bacteria in roots were significantly higher than those in stems and leaves. The dominant endophyte phyla were Cyanobacteria, Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria. The main genera detected in roots were unclassified Cyanobacteria, Rhizobium, Flavobacterium, and Sphingobium; the main genera in stems were norank_c__Cyanobacteria, Bacillus, and Pseudomonas; the main genera in leaves were norank_c__Cyanobacteria and Rhizobium. The microbiota in roots was particularly enriched in functional categories “extracellular structures” and “cytoskeleton” compared with stems and leaves (p < 0.05). Our study reveals the structural and functional characteristics of the endophytic bacteria in roots, stems, and leaves of P. polyphylla var. yunnanensis, which aids in the scientific understanding of this plant.

Assessment of the inhibition risk of paris saponins, bioactive compounds from Paris polyphylla, on CYP and UGT enzymes via cocktail inhibition assays

Paris saponins, also known as polyphyllins, are natural compounds extracted from Paris polyphylla, which have many pharmacological activities, such as anti-inflammation and anti-cancer. In particular, paris saponin I, II, VII and polyphyllin VI are the components of the quality standard for Paris polyphylla. However, the inhibition risk of polyphyllins on cytochrome P450 (CYP) and UDP-glucuronosyltransferases (UGT) remains unclear. Therefore, this report investigated the potential inhibitory effects of paris saponin I, II, VII and polyphyllin VI on the activities of CYP (CYP1A2, CYP2B1, CYP2C11, CYP2D1, CYP2E1 and CYP3A2) and UGT (UGT1A1, UGT1A3, UGT1A6, PROG and AZTG) through cocktail inhibition assays in vitro. In the study of CYP, polyphyllin VI exhibited weak inhibition on CYP2D1 activity in rat liver microsomes with IC₅₀ value at 45.2 μM, while paris saponin VII weakly inhibited CYP2C11 and CYP2E1 activities with IC₅₀ value at 42.0 and 67.7 μM, respectively. In the study of UGT, none of the four steroidal saponins showed significant inhibition risk. In conclusion, paris saponin I, II, VII and polyphyllin VI have very low potential to cause the possible toxicity and drug interactions involving CYP and UGT enzymes, indicating that they are safe enough to take with drugs.

Flavonoid aglycone-oriented data-mining in high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry: efficient and targeted profiling of flavonoids in Scutellaria barbata

The high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (HPLC-QTOF-MS/MS) technique is a powerful tool for compound identification in complex natural products. However, untargeted MS/MS data analysis needs skillful experience and sometimes neglects minor compounds, which are co-eluted with major ones or overshadowed by the matrix. Flavonoids are the main bioactive components in Scutellaria barbata, and the total flavonoid content is 47.02 ± 3.23 mg QE/g DW. Although some flavonoid aglycones and their O-glycosides have been found in S. barbata, comprehensive profiling of flavonoids is unknown. Therefore, we report a flavonoid aglycone-oriented data-mining strategy for efficient and targeted profiling of flavonoids in S. barbata. The strategy includes four steps: (1) HPLC-QTOF-MS analysis of S. barbata; (2) construction of a flavonoid aglycone-based database according to biosynthetic pathway analysis and reported data; (3) extraction of through flavonoid aglycone-based ion chromatography; (4) identification of targeted flavonoids by MS/MS analysis. As a result, 45 flavonoids, including 24 flavones, 1 flavonol, 13 flavanones, and 7 flavanonols, were unambiguously or tentatively identified, while 20 of them were reported in S. barbata for the first time. Moreover, 14 available flavonoids were sensitively, precisely, and accurately determined by standard calibration curves, with limit of detection at 0.06 to 1.55 μg/g, limit of quantification at 0.16 to 3.70 μg/g, relative standard deviation (RSD) less than 9.0% for intra- and inter-day variations, and recovery at 92.6-108.1%. The matrix did not obviously suppress or enhance the ionization of 14 flavonoids, and finally their contents ranging from 0.04 to 4.49 mg/g in S. barbata were successfully achieved. Collectively, our results demonstrate that an efficient, reliable, and valuable strategy has been provided to rapidly and sensitively screen, profile, and quantify chemical components of complex natural products. Graphical abstract.