Abstract
Fungal endophytes are a major source of anti-infective agents and other medically relevant compounds. However, their classical blinded-chemical investigation is a challenging process due to their highly complex chemical makeup. Thus, utilizing cheminformatics tools such as metabolomics and computer-aided modelling is of great help deal with such complexity and select the most probable bioactive candidates. In the present study, we have explored the fungal endophytes associated with the well-known antimalarial medicinal plant Artemisia annua for their production of further antimalarial agents. Based on the preliminary antimalarial screening of these endophytes and using LC-HRMS-based metabolomics and multivariate analyses, we suggested different potentially active metabolites (compounds 1–8). Further in silico investigation using the neural-network-based prediction software PASS led to the selection of a group of quinone derivatives (compounds 1–5) as the most possible active hits. Subsequent in vitro validation revealed emodin (1) and physcion (2) to be potent antimalarial candidates with IC50 values of 0.9 and 1.9 µM, respectively. Our approach in the present investigation therefore can be applied as a preliminary evaluation step in the natural products drug discovery, which in turn can facilitate the isolation of selected metabolites notably the biologically active ones.
Highlights
Fungal endophytes are a major source of anti-infective agents and other medically relevant compounds
Several previous studies have reported that the isolated pure compounds were less pharmacologically active than their corresponding crude extracts e.g. Artemisia annua crude extract was more active as an antimalarial agent than its marker pure metabolite, artemisinin [11]4
The amplified internal transcribed spacer (ITS) region of the fungal strains was sequenced and compared with the ITS sequences of microorganisms represented in the National Center for Biotechnology Information (NCBI) database GenBank using blast search and MEGA 7 to generate a phylogenetic tree (Fig. 2) with the method of Neighbour-joining tree algorithm and the evolutionary distances were figured using the Kimura 2-parameter method
Summary
Identity and phylogeny of isolated fungal endophytes. In total, eleven endophytic fungal isolates were recovered from healthy above-ground tissue (leaf, stem) of three plant specimens (A. annua) (Table 1). To correlate between the in vitro anti-plasmodial activity (Table 1) and the possible metabolites responsible for this observed activity, an MVA on the generated LC-HRMS data needs to be performed. Hierarchical Cluster Analysis (HCA) derived from the PCA (Fig. 3B) results revealed that extracts prepared from the same fungal genera were grouped. HCA dendrogram illustrated that endophytes belong to Aspergillus, Penicil‐ lium, and Talaromyces genera were close to each other, and significantly separated from those of Fusarium and Pleosporaceae, similar to their phylogenetic analysis (Fig. 2). This indicated that the metabolomic analysis of a group of related organisms can be used as a chemotaxonomic tool along with phylogenetic proximity analysis
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