Development of DNA barcodes for selected Acacia species by using rbcL and matK DNA markers

Acacia species are very important tree species in tropical and subtropical countries of the World for their economic and medicinal benefits. Precise identification of Acacia is very important to distinguish the invasive species from rare species however, it is difficult to differentiate Acacia species based on morphological charcters. In addition, precise identification is also important for wood charcterization in the forest industry as these species are declining due to illegal logging and deforestation. To overcome thsese limitations of morphological identification, DNA barcoding is being used as an efficient and quick approach for precise identification of tree species. In this study, we selected two chloroplast and plastid base DNA markers (rbcL and matK) for the identification of five selected tree species of Acacia (A. albida, A. ampliceps, A. catechu, A. coriacea and A. tortilis). The genomic DNA of the selected Acacia species was extracted, amplified through PCR using specific primers and subsequently sequenced through Sanger sequencing. In matK DNA marker the average AT nucleotide contents were higher (59.46%) and GC contents were lower (40.44%) as compared to the AT (55.40%) and GC content (44.54%) in rbcL marker. The means genetic distance K2P between the Acacia species was higher in matK (0.704%) as compared to rbcL (0.230%). All Acacia species could be identified based on unique SNPs profile. Based on SNP data profiles, DNA sequence based scannable QR codes were developed for accurate identification of Acacia species. The phylogenetic analysis based on both markers (rbcL and matK) showed that both A. coriacea and A. tortilis were closely related with each other and clustered in the same group while other two species A. albida and A. catechu were grouped together. The specie A. ampliceps remained ungrouped distantly, compared with other four species. These finding highlights the potential of DNA barcoding for efficient and reproducible identification of Acacia species.