Multiple regression analysis as a tool for the identification of relations between semi-quantitative LC-MS data and cytotoxicity of extracts of the fungus Fusarium avenaceum (syn. F. arthrosporioides)

Abstract

The cytotoxicity of methanolic extracts from rice cultures of 53 Fusarium avenaceum strains, which had been isolated from different host organisms in Northern Europe, Canada and Australia/New Zealand, was investigated in a rat hepatoma (H4IIE-W), porcine epithelial kidney (PK-15), foetal feline lung fibroblast, dog lymphoblast (D3447), and a human hepatocarcinoma (Hep G2) cell line using the Alamar Blue™ assay. All extracts were screened for known fungal metabolites using high-performance liquid chromatography with photodiode array and mass spectrometric detection, and both known and unknown metabolites were semi-quantified. Known metabolites that were determined in the cultures include acuminatopyrone, 2-amino-14,16-dimethyloctadecan-3-ol (2-AOD-3-ol), antibiotic Y, aurofusarin, chlamydosporol, chlamydospordiol, enniatins, fusarin A and C, and moniliformin. Multiple regression analysis was used in order to relate fungal metabolites to the cytotoxicity of the extracts. Separate linear regression models were constructed for each cell line. Eleven different fungal metabolites were related to the cytotoxicity ( P < 0.05 ). Out of these, nine metabolites were siginificantly related to the cytotoxicity in only one of the five models, while two, namely enniatins and 2-AOD-3-ol, were significant contributors in three or four regression models, respectively. This paper describes how multiple regression analysis may be applied for the assignment of bioactivity/toxicity to the constituents of a multi-component mixture.