Population genetic and phytochemical dataset of Saraca asoca: A traditionally important medicinal tree.

The data presented in this article is in support of the research paper "Genetic and phytochemical investigations for understanding population variability of the medicinally important tree Saraca asoca to help develop conservation strategies" Hegde et al., 2018. This article provides PCR based Inter-Simple Sequence Repeat (ISSR) and HPLC datasets of 106 individual samples of Saraca asoca collected from various geographical ranges of the Western Ghats of India. The ISSR data includes information on genetic diversity and images of population structures generated through amplified DNA products from samples of Saraca asoca leaf. Phytochemical data obtained from HPLC includes concentration (mg/g) of gallic acid (GA), catechin (CAT), and epicatechin (EPI). The data also presents information obtained from various statistical analysis viz. standard error of the mean values, distribution variables, prediction accuracy, and multiple logistic regression analysis.

Genetic and phytochemical investigations for understanding population variability of the medicinally important tree Saraca asoca to help develop conservation strategies.

Saraca asoca (Roxb.) De Wilde (Caesalpiniaceae) is a highly traded IUCN red listed tree species used in Ayurvedic medicines for the treatment of various disorders, especially gynaecological problems. However, information about the genetic variations between populations and corresponding variation in specialized metabolites of S. asoca remains unclear. To address this issue, we analysed 11 populations of S. asoca with 106 accessions collected from Western Ghats of India using ISSR markers along with selected phytocompounds using RP-HPLC. Twenty primers were screened, out of which seven were selected for further analysis based on generation of clear polymorphic banding patterns. These seven ISSR primers produced 74 polymorphic loci. AMOVA showed 43% genetic variation within populations and 57% among the populations of S. asoca. To estimate the genetic relationships among S. asoca populations, UPGMA and Bayesian Models were constructed, which revealed two clusters of similar grouping patterns. However, excluding minor deviations, UPGMA and dissimilarity analysis showed close association of genotypes according to their geographical locations. Catechin (CAT), epicatechin (EPI) and gallic acid (GA) were quantified from bark and leaf samples of corresponding genotypes collected from 106 accessions. ROC plots depicted the sensitivity and specificity of the concentrations of tested phytocompounds at various cut-off points. Although, multiple logistic regression analysis predicted some association between few loci with GA, EPI and CAT, but PCA for phytochemical data failed to distinguish the populations. Overall, there were no significant trends observed to distinguish the populations based on these phytocompounds. Furthermore, the study advocates the delineate provenance regions of S. asoca genotypes/chemotype snapshots for in-situ conservation and ex-situ cultivation.

Resolving Identification Issues of Saraca asoca from Its Adulterant and Commercial Samples Using Phytochemical Markers.

Saraca asoca (Roxb.) De Wilde (Ashoka) is a highly valued endangered medicinal tree species from Western Ghats of India. Besides treating cardiac and circulatory problems, S. asoca provides immense relief in gynecological disorders. Higher price and demand, in contrast to the smaller population size of the plant, have motivated adulteration with other plants such as Polyalthia longifolia (Sonnerat) Thwaites. The fundamental concerns in quality control of S. asoca arise due to its part of medicinal value (Bark) and the chemical composition. Phytochemical fingerprinting with proper selection of analytical markers is a promising method in addressing quality control issues. In the present study, high-performance liquid chromatography of phenolic compounds (gallic acid, catechin, and epicatechin) coupled to multivariate analysis was used. Five samples each of S. asoca, P. longifolia from two localities alongside five commercial market samples showed evidence of adulteration. Subsequently, multivariate hierarchical cluster analysis and principal component analysis was established to discriminate the adulterants of S. asoca. The proposed method ascertains identification of S. asoca from its putative adulterant P. longifolia and commercial market samples. The data generated may also serve as baseline data to form a quality standard for pharmacopoeias. SUMMARY: Simultaneous quantification of gallic acid, catechin, epicatechin from Saraca asoca by high-performance liquid chromatographyDetection of S. asoca from adulterant and commercial samplesUse of analytical method along with a statistical tool for addressing quality issues. Abbreviations used: HPLC: High Performance Liquid Chromatography; RP-HPLC: Reverse Phase High Performance Liquid Chromatography; CAT: Catechin; EPI: Epicatechin; GA: Gallic acid; PCA: Principal Component Analysis.