In silico route towards development of DNA barcodes of Indian medicinal plants in trade

Herbal drugs are generally traded in many processed forms, thus making their identification by traditional morphology based methods strenuous. Hence, development of effective methods for their accurate identification to control problems of adulteration, substitution and biopiracy becomes necessary. DNA barcoding, a molecular diagnostic method, capable of identifying the species with a minute amount of tissue based on the sequence variation in the selected locus/loci, could be an effective tool for this purpose. A single locus that can be used as a universal barcode for land plants has not been identified. The consensus is that the barcode for plants could be a multi-locus one. As per National Medicinal Plant Board of India, 960 medicinal plant species, belonging to 169 families and 575 genera, are traded. Developing DNA barcodes for all these species from among the loci suggested/used for barcoding of plants by wet research would be a gigantic task. Therefore, to get an initial insight in the possible combination of loci that could provide barcodes to these plants, an in silico approach was followed. Barcode quality sequences of four loci, nrITS (nuclear ribosomal internal transcribed Spacer), ITS2 (internal transcribed spacer 2), matK (maturase K) and rbcL (rubisco large subunit), of these 960 medicinal plants, available on NCBI GenBank were downloaded and checked for their species specificity by BLAST1 method. Barcode quality ITS, ITS2, matK and rbcL sequences of 217, 292, 263 and 360 medicinal plant species, respectively were available. Individually, ITS, 83.8% of which were speciesspecific, was found to be the best among the four loci, followed by matK, ITS2 and rbcL, which provided species specific barcodes to 79.8%, 77.7% and 76.3% species, respectively. At the genus level, all the four loci provided specificity above 90% with the highest of 96.3% being of ITS, followed by 95.8% of matK. Various multi-locus combinations were checked for their species or generic specificity for 144 species, sequences of all the four loci of which were available. Two-locus combinations of ITS+matK and ITS+rbcL correctly identified 95.8% and 95.1% of the species, respectively, whereas, this value for combinations of ITS2+matK and ITS2+rbcL was 93.7% and 93%, respectively. Among two locus combinations, the lowest percent species specificity was exhibited by matK+rbcL (87.5%). Use of three loci, ITS+matK+rbcL raised the species identification capability to 97.2%. All the combinations of loci, whether two-or three-locus, provided 100% or near 100% genus specificity. Thus, the present study amply demonstrates the utility of an in silico approach as an initial and important step towards development of DNA barcodes for medicinal plants.