HPTLC-Based Chemical Profiling: An Approach to Monitor Plant Metabolic Expansion Caused by Fungal Endophytes.

ABSTRACT: Fungal endophytes isolated from two latex bearing species were chosen as models to show their potential to expand their host plant chemical diversity. Thirty-three strains were isolated from Alstonia scholaris (Apocynaceae) and Euphorbia myrsinites (Euphorbiaceae). High performance thin layer chromatography (HPTLC) was used to metabolically profile samples. The selected strains were well clustered in three major groups by hierarchical clustering analysis (HCA) of the HPTLC data, and the chemical profiles were strongly correlated with the strains' colony size. This correlation was confirmed by orthogonal partial least squares (OPLS) modeling using colony size as "Y" variable. Based on the multivariate data analysis of the HPTLC data, the fastest growing strains of each cluster

SUBMITTER: Salome-Abarca LF 

PROVIDER: S-EPMC8002819 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature