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.

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.