Molecular and Phytochemical Characterizations of Cichorium intybus L. in Diverse Ecogeographical Regions of Kashmir Himalaya

Abstract

Cichorium intybus L. (chicory) is an important medicinal plant with significant economic potential and has recently gained rapid momentum in the functional food sector. In the present study, soil chemistry, phytochemical, and molecular diversity were assessed for 50 accessions of chicory collected from diverse agro-climatic zones. In total, 64 common metabolites were identified from the leaves of 7 chicory accessions collected from different altitudes and among them, the predominant metabolites included methyl commate B (6.3–10.14%), gamma sitosterol (2.79–9.3%), and 9, 12, 15-octadecatrienoic-acid (2.55–8.42%). Three terpenoid compounds, viz., betulin, kolavelool and betulinaldehyde, were observed at high altitudes (1790, 1901, and 2172 m) and not observed at low altitudes. Among these compounds, betulin had the highest concentration with an average value of 23.53% followed by kolavelool with 7.37% and betulinaldehyde with 7.21%. For molecular diversity analysis, 12 ISSR primers were selected for PCR amplification and 86 bands were generated with an overall polymorphism percentage of 67.44%. The observed Nei’s genetic diversity (H) and Shannon’s information indices (I) were highest for the Pulwama (CIN-PU) group of accessions (H = 0.222 ± 0.018; I = 0.343 ± 0.027) and lowest for the Baramulla (CIN-BM) group of accessions (H = 0.115 ± 0.019; I = 0.173 ± 0.028). The Analysis of Molecular Variance (AMOVA) analysis revealed 56% variation existing within the groups and 44% among the groups of chicory accessions. This study shows that chicory populations vary considerably in terms of their molecular and phytochemical composition as a function of their geographic location. Furthermore, this study demonstrates that chicory phytochemical and molecular diversity are significantly influenced by altitude, soil chemistry, and growing conditions. Using metabolomics and altitudinal variation, cluster analysis showed that geographic origin was correlated with diversity patterns.