Gender specific SNP markers in Coscinium fenestratum (Gaertn.) Colebr. for resource augmentation.

BACKGROUND: Unregulated extraction of highly traded medicinal plant species results in drastic decline of the natural resources and alters viable sex ratio of populations. Conservation and long-term survival of such species, require gender specific restoration programs to ensure reproductive success. However, it is often difficult to differentiate sex of individuals before reaching reproductive maturity. C. fenestratum is one of the medicinally important and overexploited dioecious woody liana, with a reproductive maturity of 15 years. Currently, no information is available to identify sex of C. fenestratum in seedling stage while augmenting the resources. Thus, the current study envisages to utilize transcriptomics approach for gender differentiation which is imperative for undertaking viable resource augmentation programmes. METHODS AND RESULTS: Gender specific SNPs with probable role in sexual reproduction/sex determination was located using comparative transcriptomics approach (sampling male and female individuals), alongside gene ontology and annotation. Nine sets of primers were synthesized from 7 transcripts (involved in sexual reproduction/other biological process) containing multiple SNP variants. Out of the nine primer pairs, only one SNP locus with no available information of its role in reproduction, showed consistent and accurate results (males-heterozygous and females-homozygous), in the analyzed 40 matured individuals of known sexes. Thus validated the efficiency of this SNP marker in differentiating male and female individuals. CONCLUSIONS: The study could identify SNPs linked to the loci with apparent role in gender differentiation. This SNP marker can be used for early sexing of seedlings for in-situ conservation and resource augmentation of C. fenestratum in Kerala, India.

Artificial intelligence in timber forensics employing DNA barcode database.

Extreme difficulties in species identification of illegally sourced wood with conventional tools have accelerated illicit logging activities, leading to the destruction of natural resources in India. In this regard, the study primarily focused on developing a DNA barcode database for 41 commercial timber tree species which are highly vulnerable to adulteration in south India. The developed DNA barcode database was validated using an integrated approach involving wood anatomical features of traded wood samples collected from south India. Traded wood samples were primarily identified using wood anatomical features using IAWA list of microscopic features for hardwood identification. Consortium of Barcode of Life (CBOL) recommended barcode gene regions (rbcL, matK & psbA-trnH) were employed for developing DNA barcode database. Secondly, we employed artificial intelligence (AI) analytical platform, Waikato Environment for Knowledge Analysis (WEKA) for analyzing DNA barcode sequence database which could append precision, speed, and accuracy for the entire identification process. Among the four classification algorithms implemented in the machine learning algorithm (WEKA), best performance was shown by SMO, which could clearly allocate individual samples to their respective sequence database of biological reference materials (BRM) with 100 % accuracy, indicating its efficiency in authenticating the traded timber species. Major advantage of AI is the ability to analyze huge data sets with more precision and also provides a large platform for rapid authentication of species, which subsequently reduces human labor and time. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03604-0.

Quantification of adulteration in traded ayurvedic raw drugs employing machine learning approaches with DNA barcode database.

Adulteration of expensive raw drugs with inferior taxa has become a routine practice, conceding the quality and safety of derived herbal products. In this regard, the study addresses the development of an integrated approach encompassing DNA barcode and HPTLC fingerprinting to authenticate chiefly traded ayurvedic raw drugs in south India [viz. Saraca asoca (Roxb.) Willd., Terminalia arjuna (Roxb. ex DC.) Wight and Arn., Sida alnifolia L. and Desmodium gangeticum (L.) DC.] from its adulterants. Consortium of Barcode of Life (CBOL) recommended DNA barcode gene regions viz. nuclear ribosomal-Internal Transcribed Spacer (nrDNA-ITS), maturase K (matK), ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL) and psbA-trnH spacer regions along with HPTLC profiling were experimented and a reference database was created. Further, an integrated analytical approach employing genetic distance-based Maximum Likelihood phylogenetic tree and Artificial Intelligence (AI)based Machine Learning Algorithms (MLA)-Waikato Environment for Knowledge Analysis (WEKA) and Barcoding with Logic (BLOG) were employed to prove efficacy of DNA barcode tool. Even though, among the four barcodes, psbA-trnH (S. alnifolia and its adulterants, T. arjuna and its adulterants) or ITS region (S. asoca and its adulterants, D. gangeticum and its adulterants) showed highest inter specific divergences in the selected Biological Reference Materials (BRMs), rbcL or matK barcode regions alone were successful for authentication of traded samples. The automated species identification techniques, WEKA and BLOG, experimented for the first time in India for raw drug validation, could achieve rapid and precise identification. A national certification agency for raw drug authentication employing an integrated approach involving a DNA barcoding tool along with standard organoleptic and analytical methods can strengthen and ensure safety and quality of herbal medicines in India. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-021-03001-5.

The efficacy of machine learning algorithm for raw drug authentication in Coscinium fenestratum (Gaertn.) Colebr. employing a DNA barcode database.

Medicinal plants are a valuable resource for traditional as well as modern medicine. Consequently huge demand has exerted a heavy strain on the existing natural resources. Due to over exploitation and unscientific collection most of the commercially traded ayurvedic plants are in the phase of depletion. Adulteration of expensive raw drugs with inferior taxa has become a common practice to meet the annual demand of the ayurvedic industry. Although there are several recommended methods for proper identification varying from the traditional taxonomic to organoleptic and physiochemical, it is difficult to authenticate ayurvedic raw drugs available in extremely dried, powdered or shredded forms. In this regard, the study addresses proper authentication and illicit trade in Coscinium fenestratum (Gaertn.) Colebr. using CBOL recommended standard barcode regions viz. nuclear ribosomal-Internally Transcribed Spacer (nrDNA- ITS), maturase K (matK), ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL), and psbA-trnH spacer regions. Further, an integrated analytical approach employing Maximum Likelihood phylogenetic tree and Machine Learning Approach, Waikato Environment for Knowledge Analysis was employed to prove efficacy of the method. The automated species identification technique, Artificial Intelligence uses the ability of computers to build models that can receive the input data and then conduct statistical analyses which significantly reduces the human labour. Concurrently, scientific management, restoration, cultivation and conservation measures should be given utmost priority to reduce the depletion of wild resources as well as to meet the rapidly increasing demand of the herbal industries.

Pitfalls and promises of raw drug identification techniques in the ayurvedic industry: an overview.

India, with a rich heritage of floral diversity, is well-known for its medicinal plant wealth and is the largest producer of medicinal herbs in the world. Ethnobiological Survey of Ministry of Environment and Forests (MOEF) could identify 8000 plant species utilized in various systems of medicine with approximately 25,000 effective herbal formulations. The extensive consumption to meet demand-supply ratio exerts a heavy strain on the existing resources. This subsequently led to the adulteration and substitution of medicinal plants with look-alike species. The consumer's faith on herbal medicine is in the phase of decline due to the extremities in adulteration/substitution and ensuing consequences. It is imperative to bring forth universally acceptable standard tools to authenticate raw drugs before being processed further into formulations. A vast array of techniques such as physical, chemical (analytical), biochemical, anatomical, organoleptic, and recently emerged DNA based molecular methods are widely used for plant species authentication. In recent years, DNA barcoding has made remarkable progress in the field of medicinal plants research. DNA metabarcoding is the latest development for qualitative evaluation of the herbal formulations, whereas for quantitative analysis, combination of pharmacognostic, pharmacovigilance and analytical methods are inevitable for authentication. This review addresses the overall strengths and shortcomings of the existing as well as recently emerged techniques in authenticating ayurvedic raw drugs.