DNA meta-barcoding using rbcL based mini-barcode revealed presence of unspecified plant species in Ayurvedic polyherbal formulations

DNA barcoding of herbal supplements on the US commercial market associated with the purported treatment of COVID-19

INTRODUCTION

The COVID-19 pandemic was associated with an increased global use of traditional medicines, including Ayurvedic herbal preparations. Due to their growing demand, their processed nature, and the complexity of the global supply chain, there is an increased risk of adulteration in these products.

OBJECTIVES

The objective of this study was to assess the use of DNA barcoding for species identification in herbal supplements on the US market associated with the Ayurvedic treatment of respiratory symptoms.

METHODS

A total of 54 commercial products containing Ayurvedic herbs were tested with four DNA barcoding regions (i.e., rbcL, matK, ITS2, and mini-ITS2) using two composite samples per product. Nine categories of herbs were targeted: amla, ashwagandha, cinnamon, ginger, guduchi, tribulus, tulsi, turmeric, and vacha.

RESULTS

At least one species was identified in 64.8% of products and the expected species was detected in 38.9% of products. Undeclared plant species, including other Ayurvedic herbs, rice, and pepper, were detected in 19 products, and fungal species were identified in 12 products. The presence of undeclared plant species may be a result of intentional substitution or contamination during harvest or processing, while fungal DNA was likely associated with the plant material or the growing environment. The greatest sequencing success (42.6-46.3%) was obtained with the matK and rbcL primers.

CONCLUSION

The results of this study indicate that a combination of genetic loci should be used for DNA barcoding of herbal supplements. Due to the limitations of DNA barcoding in identification of these products, future research should incorporate chemical characterization techniques.

A combined approach of DNA metabarcoding collectively enhances the detection efficiency of medicinal plants in single and polyherbal formulations

Introduction

Empirical research has refined traditional herbal medicinal systems. The traditional market is expanding globally, but inadequate regulatory guidelines, taxonomic knowledge, and resources are causing herbal product adulteration. With the widespread adoption of barcoding and next-generation sequencing, metabarcoding is emerging as a potential tool for detecting labeled and unlabeled plant species in herbal products.

Methods

This study validated newly designed rbcL and ITS2 metabarcode primers for metabarcoding using in-house mock controls of medicinal plant gDNA pools and biomass pools. The applicability of the multi-barcode sequencing approach was evaluated on 17 single drugs and 15 polyherbal formulations procured from the Indian market.

Results

The rbcL metabarcode demonstrated 86.7% and 71.7% detection efficiencies in gDNA plant pools and biomass mock controls, respectively, while the ITS2 metabarcode demonstrated 82.2% and 69.4%. In the gDNA plant pool and biomass pool mock controls, the cumulative detection efficiency increased by 100% and 90%, respectively. A 79% cumulative detection efficiency of both metabarcodes was observed in single drugs, while 76.3% was observed in polyherbal formulations. An average fidelity of 83.6% was observed for targeted plant species present within mock controls and in herbal formulations.

Discussion

In the present study, we achieved increasing cumulative detection efficiency by combining the high universality of the rbcL locus with the high-resolution power of the ITS2 locus in medicinal plants, which shows applicability of multilocus strategies in metabarcoding as a potential tool for the Pharmacovigilance of labeled and unlabeled plant species in herbal formulations.

DNA barcoding in herbal medicine: Retrospective and prospective

DNA barcoding has been widely used for herb identification in recent decades, enabling safety and innovation in the field of herbal medicine. In this article, we summarize recent progress in DNA barcoding for herbal medicine to provide ideas for the further development and application of this technology. Most importantly, the standard DNA barcode has been extended in two ways. First, while conventional DNA barcodes have been widely promoted for their versatility in the identification of fresh or well-preserved samples, super-barcodes based on plastid genomes have rapidly developed and have shown advantages in species identification at low taxonomic levels. Second, mini-barcodes are attractive because they perform better in cases of degraded DNA from herbal materials. In addition, some molecular techniques, such as high-throughput sequencing and isothermal amplification, are combined with DNA barcodes for species identification, which has expanded the applications of herb identification based on DNA barcoding and brought about the post-DNA-barcoding era. Furthermore, standard and high-species coverage DNA barcode reference libraries have been constructed to provide reference sequences for species identification, which increases the accuracy and credibility of species discrimination based on DNA barcodes. In summary, DNA barcoding should play a key role in the quality control of traditional herbal medicine and in the international herb trade.

A novel biological sources consistency evaluation method reveals high level of biodiversity within wild natural medicine: A case study of Amynthas earthworms as “Guang Dilong”

For wild natural medicine, unanticipated biodiversity as species or varieties with similar morphological characteristics and sympatric distribution may co-exist in a single batch of medical materials, which affects the efficacy and safety of clinical medication. DNA barcoding as an effective species identification tool is limited by its low sample throughput nature. In this study, combining DNA mini-barcode, DNA metabarcoding and species delimitation method, a novel biological sources consistency evaluation strategy was proposed, and high level of interspecific and intraspecific variations were observed and validated among 5376 Amynthas samples from 19 sampling points regarded as "Guang Dilong" and 25 batches of proprietary Chinese medicines. Besides Amynthas aspergillum as the authentic source, 8 other Molecular Operational Taxonomic Units (MOTUs) were elucidated. Significantly, even the subgroups within A. aspergillum revealed here differ significantly on chemical compositions and biological activity. Fortunately, this biodiversity could be controlled when the collection was limited to designated areas, as proved by 2796 "decoction pieces" samples. This batch biological identification method should be introduced as a novel concept regarding natural medicine quality control, and to offer guidelines for in-situ conservation and breeding bases construction of wild natural medicine.

Applications of machine learning in pine nuts classification

Pine nuts are not only the important agent of pine reproduction and afforestation, but also the commonly consumed nut with high nutritive values. However, it is difficult to distinguish among pine nuts due to the morphological similarity among species. Therefore, it is important to improve the quality of pine nuts and solve the adulteration problem quickly and non-destructively. In this study, seven pine nuts (Pinus bungeana, Pinus yunnanensis, Pinus thunbergii, Pinus armandii, Pinus massoniana, Pinus elliottii and Pinus taiwanensis) were used as study species. 210 near-infrared (NIR) spectra were collected from the seven species of pine nuts, five machine learning methods (Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB)) were used to identify species of pine nuts. 303 images were used to collect morphological data to construct a classification model based on five convolutional neural network (CNN) models (VGG16, VGG19, Xception, InceptionV3 and ResNet50). The experimental results of NIR spectroscopy show the best classification model is MLP and the accuracy is closed to 0.99. Another experimental result of images shows the best classification model is InceptionV3 and the accuracy is closed to 0.964. Four important range of wavebands, 951–957 nm, 1,147–1,154 nm, 1,907–1,927 nm, 2,227–2,254 nm, were found to be highly related to the classification of pine nuts. This study shows that machine learning is effective for the classification of pine nuts, providing solutions and scientific methods for rapid, non-destructive and accurate classification of different species of pine nuts.