Authentication of Three Source Spices of Arnebiae Radix Using DNA Barcoding and HPLC

Rapid identification and determination of adulteration in medicinal Arnebiae Radix by combining near infrared spectroscopy with chemometrics

The medicinal Arnebia Radix (AR) is one of widely-used Chinese herbal medicines (CHMs), usually adulterated with non-medicinal species that seriously compromise the quality of AR and affect patients' health. Detection of these adulterants is usually performed by using expensive and time-consuming analytical instruments. In this study, a rapid, non-destructive, and effective method was proposed to identify and determine the adulteration in the medicinal AR by near-infrared (NIR) spectroscopy coupled with chemometrics. 37 batches of medicinal AR samples originated from Arnebia euchroma (Royle) Johnst., 11 batches of non-medicinal AR samples including Onosma paniculatum Bur. et Franch and Arnebia benthamii (Wall. ex G. Don) Johnston, and 72 batches of adulterated AR samples were characterized by NIR spectroscopy. The data driven-soft independent modeling by class analogy (DD-SIMCA) and partial least squares-discriminant analysis (PLS-DA) were separately used to differentiate the authentic from adulterated AR samples. Then the PLS and support vector machine (SVM) were applied to predict the concentration of the adulteration in the adulterated AR samples, respectively. As a result, the classification accuracies of DD-SIMCA and PLS-DA models were 100% for the calibration set, and 96.7% vs. 100% for the prediction set. Moreover, the relative prediction deviation (RPD) values of PLS models reached 11.38 and 7.75 for quantifying two adulterants species, which were obviously superior to the SVM models. It can be concluded that the NIR spectroscopy coupled with chemometrics is feasible to identify the authentic from adulterated AR samples and quantify the adulteration in adulterated AR samples.

Chemical Relationship among Genetically Authenticated Medicinal Species of Genus Angelica

The genus Angelica comprises various species utilized for diverse medicinal purposes, with differences attributed to the varying levels or types of inherent chemical components in each species. This study employed DNA barcode analysis and HPLC analysis to genetically authenticate and chemically classify eight medicinal Angelica species (n = 106) as well as two non-medicinal species (n = 14) that have been misused. Nucleotide sequence analysis of the nuclear internal transcribed spacer (ITS) region revealed differences ranging from 11 to 117 bp, while psbA-trnH showed variances of 3 to 95 bp, respectively. Phylogenetic analysis grouped all samples except Angelica sinensis into the same cluster, with some counterfeits forming separate clusters. Verification using the NCBI database confirmed the feasibility of species identification. For chemical identification, a robust quantitative HPLC analysis method was developed for 46 marker compounds. Subsequently, two A. reflexa-specific and seven A. biserrata-specific marker compounds were identified, alongside non-specific markers. Moreover, chemometric clustering analysis reflecting differences in chemical content between species revealed that most samples formed distinct clusters according to the plant species. However, some samples formed mixed clusters containing different species. These findings offer crucial insights for the standardization and quality control of medicinal Angelica species.

Implementation of machine learning in DNA barcoding for determining the plant family taxonomy

The DNA barcoding approach has been used extensively in taxonomy and phylogenetics. The differences in certain DNA sequences are able to differentiate and help classify organisms into taxa. It has been used in cases of taxonomic disputes where morphology by itself is insufficient. This research aimed to utilize hierarchical clustering, an unsupervised machine learning method, to determine and resolve disputes in plant family taxonomy. We take a case study of Leguminosae that historically some classify into three families (Fabaceae, Caesalpiniaceae, and Mimosaceae) but others classify into one family (Leguminosae). This study is divided into several phases, which are: (i) data collection, (ii) data preprocessing, (iii) finding the best distance method, and (iv) determining disputed family. The data used are collected from several sources, including National Center for Biotechnology Information (NCBI), journals, and websites. The data for validation of the methods were collected from NCBI. This was used to determine the best distance method for differentiating families or genera. The data for the case study in the Leguminosae group was collected from journals and a website. From the experiment that we have conducted, we found that the Pearson method is the best distance method to do clustering ITS sequence of plants, both in accuracy and computational cost. We use the Pearson method to determine the disputed family between Leguminosae. We found that the case study of Leguminosae should be grouped into one family based on our research.

Rapid identification of chemical compositions from three species of Siegesbeckiae Herba by ultra-performance liquid chromatography-electrospray ionization-quadrupole time of flight-mass spectrometry in combination with deoxyribonucleic acid barcoding

Siegesbeckiae Herba, a traditional Chinese medicine, originates from Siegesbeckia orientalis, S. glabrescens, and S. pubescens in the Pharmacopoeia of the People's Republic of China. However, accurate identification of decoction pieces from the three plants remains a challenge. In this study, 26 batches of Siegesbeckiae Herba were identified by deoxyribonucleic acid barcoding, and their chemical compositions were determined using ultra-performance liquid chromatography-electrospray ionization-quadrupole time of flight-mass spectrometry. The results showed that the internal transcribed spacer 2 and internal transcribed spacer 1-5.8 S- internal transcribed spacer 2 sequences could distinguish three species. In total, 48 compounds were identified including 12 marker compounds screened for three species using the partial least square discriminant analysis. Among these, two diterpenoids 16-O-malonylkirenol and 15-O-malonylkirenol, and a novel diterpenoid 15,16-di-O-malonylkirenol were isolated and identified. A convenient method for the identification of Siegesbeckiae Herba was established using kirenol and 16-O-acetlydarutoside as control standards by thin-layer chromatography. Unexpectedly, none of the batches of S. orientalis contained kirenol, which did not meet the quality standards of Siegesbeckiae Herba, suggesting that the rationality of kirenol as a quality marker for S. orientalis should be further investigated. The results of this study will contribute to the quality control of Siegesbeckiae Herba.

Authentication, chemical profiles analysis, and quality evaluation of corn silk via DNA barcoding and UPLC-LTQ/Orbitrap MS chemical profiling

Corn silk is commonly consumed in teas, food ingredients, and herbal medicines. Several varieties of corn silk are grown in different habitats in China. However, as information regarding their phytochemistry and genetic diversity is limited, their medicinal potential has not been utilized thoroughly. Thus, we aimed to use a combination of DNA barcoding based on specific primer ITSC sequences and ultra-performance liquid chromatography coupled with linear trap quadrupole-Orbitrap mass spectrometry (UPLC-LTQ/Orbitrap MS) approach for identifying and evaluating corn silk. ITSC barcoding helped us to identify that 52 samples could be classified into 7 groups of corn silk varieties, but the widely used nrITS and psbA-trnH barcodes failed to identify these varieties. UPLC-LTQ/Orbitrap MS was used to study the components in alcohol extracts derived from different corn silk varieties, and the detected chemical components were analyzed via bioinformatics techniques. We proposed 199 components using untargeted UPLC-LTQ/Orbitrap MS-based metabolomics analysis and identified 67 components. PCA and OPLS-DA analysis revealed two distinct chemotypes by selecting 27 components that could act as difference indicators. KEGG analysis showed that the 199 components were enriched in 12 metabolic pathways. The results showed that corn silk is rich in many types of chemicals and DNA barcoding is better than UPLC-LTQ/Orbitrap MS in distinguishing the differences between different varieties of corn silk. Our findings provide new insights into the chemical and molecular characteristics of different varieties of corn silk, which play a crucial role in the utilization of corn silk resources.

Application of DNA barcoding to the entire traditional Chinese medicine industrial chain: A case study of Rhei Radix et Rhizoma

BACKGROUND

Safety concerns, caused by complex and unpredictable adulterants, run through the entire industrial chain of traditional Chinese medicines (TCMs). However, the conventional circulation traceability system only focuses on a certain end or link at the back end of the TCM industrial chain, ignoring the integrity of the links cross the entire industrial chain and lacking traceability. In consequence, a strict and rational supervision system is urgently required for the entire industrial chain.

HYPOTHESIS/PURPOSE

We hypothesize that DNA barcoding would be a suitable measure for the traceability of adulterants in the entire TCM industrial chain.

METHODS

In this study, Rhei Radix et Rhizoma was selected as a model to establish a traceability system for the entire TCM industrial chain. A total of 110 samples, including leaves, seeds, roots, decoction pieces, and traditional Chinese patent medicines (TCPMs), were collected upstream, midstream, and downstream of the entire industrial chain of Rhei Radix et Rhizoma. The ndhF-rpl32 fragment rather than the universal DNA barcodes, which could not distinguish the three original species of Rhei Radix et Rhizoma, was selected as a specific DNA barcode to evaluate the practical application of DNA barcoding in the chain.

RESULTS

The results showed that the ndhF-rpl32 fragment in all samples could be amplified and bi-directionally sequenced. Based on the standard operating procedures of DNA barcoding, the ndhF-rpl32 fragment clearly distinguished the seven Rheum species collected upstream of the entire industrial chain. For the samples collected midstream and downstream of the entire industrial chain, 25% of the 36 commercial decoction pieces samples were identified as adulterants, whereas the eight TCPM samples were all derived from genuine Rhei Radix et Rhizoma.

CONCLUSIONS

This study shows that DNA barcoding is a powerful and suitable technology that can be applied to trace TCMs in the entire industrial chain, thereby assuring clinical medication safety.

Discrimination of Zicao Samples Based on DNA Barcoding and HPTLC Fingerprints, and Identification of (22E)-Ergosta-4,6,8(14),22-tetraen-3-one As a Marker Compound

The unambiguous identification of plant material is a prerequisite of rational phytotherapy. Misidentification can even cause serious health problems, as in the case of the Chinese medicinal herb Zicao. Commercial material labelled "Zicao" may be derived from the roots of Arnebia euchroma (ruan zicao), Lithospermum erythrorhizon (ying zicao), or Onosma paniculata (dian zicao). All of these roots contain shikonin derivatives as main bioactive constituents, but ying zicao and dian zicao contain also hepatotoxic pyrrolizidine alkaloids in high amounts. Therefore, the use of A. euchroma with a very low pyrrolizidine alkaloid content is desirable. Confusions of the species occur quite often, indicating an urgent need for an unambiguous identification method. Discrimination of 23 zicao samples has been achieved by analyses of the nuclear internal transcribed spacer ITS2 and trnL-F intergenic spacer of the chloroplast DNA. Data were analyzed using Bioedit, ClustalX, Mega 11 and BLAST. Results indicate that ITS2 barcoding can accurately distinguish Arnebia euchroma from their adulterants. Subsequently, an HPTLC method has been developed allowing a chemical discrimination of the most widely used species. (22E)-Ergosta-4,6,8(14),22-tetraen-3-one has been identified as characteristic marker compound, allowing an unambiguous discrimination of A. euchroma and L. erythrorhizon.

Phylogenomics and Genetic Diversity of Arnebiae Radix and Its Allies (Arnebia, Boraginaceae) in China

Arnebiae Radix is a traditional medicine with pleiotropic properties that has been used for several 100 years. There are five species of Arnebia in China, and the two species Arnebia euchroma and Arnebia guttata are the source plants of Arnebiae Radix according to the Chinese Pharmacopoeia. Molecular markers that permit species identification and facilitate studies of the genetic diversity and divergence of the wild populations of these two source plants have not yet been developed. Here, we sequenced the chloroplast genomes of 56 samples of five Arnebia species using genome skimming methods. The Arnebia chloroplast genomes exhibited quadripartite structures with lengths from 149,539 and 152,040 bp. Three variable markers (rps16-trnQ, ndhF-rpl32, and ycf1b) were identified, and these markers exhibited more variable sites than universal chloroplast markers. The phylogenetic relationships among the five Arnebia species were completely resolved using the whole chloroplast genome sequences. Arnebia arose during the Oligocene and diversified in the middle Miocene; this coincided with two geological events during the late Oligocene and early Miocene: warming and the progressive uplift of Tianshan and the Himalayas. Our analyses revealed that A. euchroma and A. guttata have high levels of genetic diversity and comprise two and three subclades, respectively. The two clades of A. euchroma exhibited significant genetic differences and diverged at 10.18 Ma in the middle Miocene. Three clades of A. guttata diverged in the Pleistocene. The results provided new insight into evolutionary history of Arnebia species and promoted the conservation and exploitation of A. euchroma and A. guttata.

Chemotaxonomic Classification of Peucedanum japonicum and Its Chemical Correlation with Peucedanum praeruptorum, Angelica decursiva, and Saposhnikovia divaricata by Liquid Chromatography Combined with Chemometrics

The roots of Peucedanum japonicum (Apiaceae) have been used as an alternative to the roots of Saposhnikovia divaricata (Apiaceae) to treat common cold-related symptoms in Korea. However, a variety of Peucedanum species, including the roots of P. praeruptorum or Angelica decursiva (=P. decursivum), have been used to treat phlegm-heat-induced symptoms in China. Hence, as there are differences in the medicinal application of P. japonicum roots between Korea and China, chemotaxonomic classification of P. japonicum was evaluated. Sixty samples derived from P. japonicum, P. praeruptorum, A. decursiva, and S. divaricata were phylogenetically identified using DNA barcoding tools, and chemotaxonomic correlations among the samples were evaluated using chromatographic profiling with chemometric analyses. P. japonicum samples were phylogenetically grouped into the same cluster as P. praeruptorum samples, followed by S. divaricata samples at the next cluster level, whereas A. decursiva samples were widely separated from the other species. Moreover, P. japonicum samples showed higher chemical correlations with P. praeruptorum samples or A. decursiva samples, but lower or negative chemical correlations with S. divaricata samples. These results demonstrate that P. japonicum is more genetically and chemically relevant to P. praeruptorum or A. decursiva and, accordingly, the medicinal application of P. japonicum might be closer to the therapeutic category of these two species than that of S. divaricata.